text:
\section{Introduction}
Federated learning (FL) is a rapidly growing field in the machine learning community. The reason for this popularity is because of its \textit{decentralized} and \textit{private} nature. Model training in FL is distributed over multiple nodes where each node could be an independent piece of hardware with its own isolated data. This unique feature enables building high-quality models that benefit from external resources without requiring access to them.

Although FL is a relatively new field \citep{mcmahan2017communication}, it has drawn researchers' attention and the community has witnessed a rapid growth. Fields such as computer vision have adapted quickly to the FL framework \citep{geyer2017differentially,lin2017deep,hardy2019md,geiping2020inverting,Ren2021GRNNGR}, but others, such as natural language processing (NLP), have not been as quick and only recently have begun to explore \citep{wu2020fedmed}.

We believe that the reason for this slow(er) integration of NLP and FL is because representation learning in NLP is complicated and downstream tasks require large and data-hungry models. These requirements are heavy-handed for any FL model and slow down the unification. However, real-world NLP problems necessitate the use of distributed solutions with privacy-preserving characteristics \citep{9084352}. Our work is an effort towards leveraging FL-based solutions in NLP.

In this paper, we focus on NMT to combine it with FL. A review of the NLP literature shows that almost all recent groundbreaking architectures have been first proposed, or at least evaluated, for translation \cite{bahdanau2014neural,sutskever2014sequence,gehring2017convolutional,vaswani2017attention}. This is an indication of the intricacy of the NMT task. Therefore, it is fair to claim that any FL technique that is capable of training high-quality NMT models could also be considered as a trustworthy alternative for other NLP tasks. Thus, NMT could be a strong candidate for FL benchmarking purposes.

NMT also has other unique features that can directly benefit FL. One key factor in a fair simulation of FL is to mimic data heterogeneity (also referred to as non-IIDness) in experimental environments \citep{kim2020on}. Different sampling techniques have been proposed to model such data distribution processes \citep{ji2020dynamic,li2021federated,wang2021device}, however there is no guarantee that what we simulate is what we encounter in real-world applications. NMT, to some extent, solves this problem since parallel datasets by nature have such heterogeneity. There are only a few NLP tasks that can provide as large and diverse training corpora as NMT. For some language pairs, there exist multiple datasets with hundreds of thousands or even millions of parallel sentences.\footnote{\url{https://opus.nlpl.eu/}} This means, we should have enough data for each FL node. Moreover, each node can naturally pick one dataset/language, so we do not have to artificially distribute data. NMT, together with offering rich training data, also has a bi-lingual setting which is a compelling testbed for FL. Coping with the complexity of not only different domains, but also different languages, at the same time in a distributed platform is worth investigating.

While NMT offers a natural testbed for FL, we argue that the task itself benefits from the offerings of FL. Training a mixed-domain translation model is a challenging task.
We show that the aggregation phase of FL can greatly help with this challenge, as it efficiently fuses information from different domains. From the privacy perspective, NMT can also benefit from FL. In fact, the necessity of having a private training pipeline is relatively understudied in NMT. It is mostly assumed that all training datasets are available in a homogeneous and compatible format through a central repository, which is usually not attainable in real world. All these reasons make NMT a compelling case to study in the context of FL.

\subsection{Research Scope}
The goal of this paper is to provide preliminary results on the combination of FL and NMT, as opposed to running a comprehensive FL research or comparing different algorithms. We are also interested in studying the feasibility of training complex and deep NMT models in \textit{decentralized} and \textit{private} settings. Besides providing a set of benchmarking results, this paper's other two contributions can be summarized as follows:
\begin{itemize}
    \item We show that FL aggregation techniques are reasonable alternatives to fuse information from multiple domains, thus FL-based training could be considered as an approach to build mixed-domain NMT engines.

    \item We show that large NMT models are hard to distribute within the FL network. Therefore, we propose a novel and cost-efficient solution to reduce the communication bandwidth.
\end{itemize}

\section{Federated Learning}\label{background}
FL is an approach to train models in a distributed fashion where nodes do not (and are not allowed to) access each other's data \citep{yang2019federated,9084352}. Any node by itself is not powerful enough to deliver high-quality services due to the small size of its local data. It can perform well on in-domain instances, but it might fail to respond to requests from other domains. FL establishes a communication methodology and a platform that allows participating nodes to exchange parameters (but not data) to help boost each other's performance.

In this work, we follow the \textit{cross-silo} FL setting as outlined in \citet{li2019survey}. Algorithm \ref{algo-1} summarizes the entire model training pipeline and explains what we mean by being cross-silo.
\begin{algorithm}[h]
\DontPrintSemicolon
\SetArgSty{textnormal}
\SetKw{KwBy}{by}
 \For{$r\gets0$ \KwTo \textit{rounds} \KwBy $1$}{
    updates = {\fontfamily{pcr}\selectfont Pull(}\hspace{0.5mm}$\mathcal{C}${\fontfamily{pcr}\selectfont)}\;
    \For{ \textit{upd} \text{\textbf{in}} \textit{updates}}{
    $\mathcal{S}$ =  \textit{aggregate}\hspace{0.5mm}($\mathcal{S}$,\textit{upd})\;
    }
    $\mathcal{C}$ = {\fontfamily{pcr}\selectfont Push(}\hspace{0.5mm}$\mathcal{S}${\fontfamily{pcr}\selectfont)}\;
    }
\caption{\label{algo-1}Cross-Silo FL}
\end{algorithm}

In this setup, there is a central node $\mathcal{S}$ that orchestrates training. In each round $r$, the server pulls local updates (i.e. a set of parameters) from different nodes (also known as clients) and updates the parameters of the central model. $\mathcal{C} = \{c_1, ..., c_K\}$ indicates the set of $K$ clients. Once all information is aggregated, parameters of the central model are pushed back to clients so that they can also benefit from global/community knowledge.

One key factor in FL is \textit{communication}, which is defined by the {\fontfamily{pcr}\selectfont Pull} and {\fontfamily{pcr}\selectfont Push} steps in this algorithm. Due to the distributed nature of FL, nodes need to connect and exchange information and this needs to be carried out in an efficient fashion. The communication cost becomes even more critical when exchanging large models, such as in NMT. In the next sections, we discuss how communication directly affect the feasibility and performance of any FL setting, and how we improve it by our \textit{dynamic pulling} technique.

Algorithm \ref{algo-1} only shows the computation that occurs on the server side. It should be noted that each client is an independent silo that updates its internal model with local data. This algorithm only illustrates the main skeleton of the cross-silo setting and does not entail all the details of each step.

In our experiments, we use the well-known {\fontfamily{pcr}\selectfont FedAVG} algorithm \citep{mcmahan2017communication} for \textit{aggregation}. Therefor, Line 4 can be formulated as in Equation \ref{eq1}:
\begin{equation}\label{eq1}
    w_{r} \leftarrow \sum_{k=1}^{K} \frac{n_k}{n} w^k_r
\end{equation}
where $w_r$ is the set of all parameters of the central model in the \textit{r}-\textit{th} round, $n_k$ is the number of data points in the \textit{k}-th client's dataset, and $n$ is the total number of all training samples. There exist multiple extensions to {\fontfamily{pcr}\selectfont FedAVG}, but since it is a widely-acceptable baseline for benchmarking purposes we also use it in our experiments. This choice allows us to minimize the impact of different factors introduced by other FL algorithms and only focus on the relation between NMT and FL and their mutual impact on each other.

\section{Federated Learning for NLP}
There are several models in the field that have been proposed to leverage FL for NLP. \citet{hartmann2019federated} studied whether they could improve the ranking of suggestions in the Firefox URL bar and train a model on user interactions without violating user privacy. They incorporated feedback received from different clients using {\fontfamily{pcr}\selectfont FedAVG} which resulted in significant improvements.
\citet{ji2019learning} suggested that the simple averaging strategy used in {\fontfamily{pcr}\selectfont FedAVG} might not be sufficient enough, so they improved the aggregation phase by incorporating the significance of each client by using an attention mask to weigh clients.
\citet{chen2019federated} focused on language modelling and addressed the problem of out-of-vocabulary entries when working with different clients.

\citet{bui2019federated} investigated the effect of FL in training better and more personalized user/data representations. Their results show that when aggregating information via FL, the model quality increases significantly; at the same time, the training pipeline is distributed and private. We also make a similar observation in our experiments, though not at the representation level, but in terms of the final translation quality.

\citet{ge2020fedner} proposed a named-entity recognition (NER) model that is trained with FL to work on medical data. Their results demonstrate that not only FL preserves privacy, but also outperforms models trained in a centralized fashion. Apart from NER, models with similar concerns have been proposed for mobile keyword prediction \citep{Hard2018FederatedLF}, keyword spotting \citep{leroy2019federated}, and next word prediction \citep{stremmel2020pretraining}.

\citet{liu2020federated} utilized FL to pre-train and/or fine-tune BERT models \citep{devlin2018bert}. From a research standpoint, it is worthwhile to understand if it is even feasible to handle such deep models in an FL framework, and whether a simple averaging-based aggregation is enough. They attempted to address these questions and provided supporting results. In that sense, our work is similar to theirs as we also work with complex models. In addition, we also discuss the bandwidth problem to facilitate exchanging large sets of parameters

\subsection{Domain Adaptation}\label{da}
Domain adaptation covers a wide range of problems from adjusting a model to work in a new domain/genre \citep{chu-wang-2018-survey} to fine-tuning for noisier conditions \citep{passban2020revisiting}, or even transferring a model to a different environment for a different task \citep{zhu2020transfer}. Domain adaptation has recently attracted more attention due to advances introduced by models such as ELMO \citep{Peters:2018} and BERT \citep{devlin2018bert}. These models provide general-purpose representations which are easily adaptable to other tasks. In these models, \textit{all} network parameters are fine-tuned during adaptation, which might not be necessary. \citet{houlsby2019parameter}, \citet{pfeiffer2020adapterhub}, and \citet{ruckle2020adapterdrop} proposed a new set of architectures, known as Adapters, to tackle this problem. Adapters are low-cost plug-ins that are mounted on pre-trained models, so when adapting the model only these small sets of parameters are updated.

\citet{bapna2019simple} proposed an NMT variation of Adapters. In their model, a dedicated component is added inside each layer that is responsible for transitioning in-between domains. However, all these solutions perform in centralized settings. \citet{Roosta2021CommunicationEfficientFL} studied this problem in the context of FL and showed that Adapters might not be aligned well with the distributed nature of FL. As they reported, Adapters seem to be suitable to connect two domains but when exposed to several domains in FL, they diverge too much from their main distribution, such that using them in the body of clients drastically downgrades performance.

To address the problem, they introduced additional and dedicated layers (as opposed to intra-layer modules in Adapters), called Controllers, that are designed to be exchanged between the server and clients. These new layers are placed in-between client model's original layers and deal with external information sent/received to/from the server. Since they only exchange Controllers, they are able to reduce the communication bandwidth. This work is the closest to ours so we use it as our main baseline.

The model proposed by \citet{Roosta2021CommunicationEfficientFL} suffers from two issues. They randomly introduce new layers (Controllers) but there is no mechanism defined to determine the number those layers, i.e. it is not investigated that how many Controllers are required under different conditions. It is also not clear where these layers need to be placed, and it is only discovered through experimental explorations. On the contrary, we introduce a simple yet effective heuristic to select a subset of impactful parameters during communication. In our solution, we do not need to deal with the aforementioned issues.

\section{Low-Cost Domain Adaptation in FL}\label{method}
In our FL setting, we initialize each client with a high-quality but generic NMT engine. Clients use local data to fine-tune their internal model and the combination of a pre-loaded model with local data should lead to better quality. Clients also connect with the server regularly to transfer local knowledge and contribute to the aggregation phase. In such a process, domain adaptation happens naturally. Inspired by \citet{Roosta2021CommunicationEfficientFL}, we implemented this idea and observed a substantial boost in our translation engines. Not only are we able to deliver better results but also we train NMT models in a distributed and private fashion. However, we noticed that \textit{communication} could be quite costly.

In the default configuration, for every {\fontfamily{pcr}\selectfont Pull} (in Algorithm \ref{algo-1}) a large NMT engine has to be exchanged, which might not be necessary. In order to clarify why, we designed an experiment whose information is illustrated in Figure \ref{fig:diff}. In this experiment, we pick three of our engines and train them for $120$K steps within the FL pipeline. For each model, we compare tensors from the $120$K checkpoint to their $110$K peers and measure how much they changed in-between these two checkpoints.  More specifically, for a given tensor $w$, we compute the pair-wise difference between values from the two checkpoints ($w_{d} = w_{120K} - w_{110K}$), then compute the absolute-value norm of the difference tensor ($\norm{w_{d}}$). The norm value indicates the shift of each tensor during FL rounds. Figure \ref{fig:diff} provides a histograms of norm values that belong to different tensors from the encoders and decoders of our translation engines (for more information about the engines and datasets see Section \ref{exp}).
\begin{figure}[htp]
    \centering
    \includegraphics[width=0.37\textwidth]{diff-tensor-enc-dec}
    \caption{The histograms of the norm values of the difference tensors computed for the $110$K and $120$K checkpoints. The \textit{x} and \textit{y} axes show the \textit{norm} and the \textit{number of tensors}, respectively. Information related to encoder tensors is visualized in the upper half and the bottom sub-figure consists of decoder tensors' information. The first blue bar of the encoder sub-figure indicates that around $40$ tensors in the WMT encoder only changed slightly from the checkpoint $110$K to $120$K as their norm is in the range [0,5], whereas the last bar on the other end of the same sub-figure shows that around $2$ tensors in the
    WMT model changed drastically as the norm of their difference tensors is close to $175$.}
    \label{fig:diff}
\end{figure}

Results from Figure \ref{fig:diff}, together with our other observations, show that a small subset of tensors diverge substantially (mostly shown in the right half of the figures), but for the rest, there is a heavy concentration around small norms. More interestingly, we realized that this is a pattern that consistently occurs from one round to the next in our FL experiments, namely each tensor either belongs to a set of highly-fluctuating parameters or it only changes marginally and lies in the less active set. The fluctuation threshold can change but tensors almost always stay in their respective clusters between rounds. We used this finding as a basis of our design to improve bandwidth consumption, such that we decided to focus on either highly fluctuating tensors or those in the other cluster and only {\fontfamily{pcr}\selectfont Pull} one type of tensors during communication. The strategy is simple but has led to promising results.

More formally, a variation of the aforementioned idea can be simply formulated as shown in Equation \ref{eq:dp-gt}:
\begin{equation} \label{eq:dp-gt}
\begin{split}
DP_{g}^{c} = \{w_r^t; \norm{w_r^t - w_{r-1}^t} \geq \theta \} \\
\end{split}
\end{equation}
$DP_{g}^{c}$ in the \textit{r}-\textit{th} round consists of all tensors ($w^t_r$) that deviate from their previous values in the previous round by $\theta$. \textit{DP} stands for \textit{Dynamic Pulling} and \textit{g} indicates that the difference norm of candidate tensors should be \textit{greater than or equal} to the threshold. \textit{DP} is exclusively computed for each client which is specified with the \textit{c} superscript. Moreover, the \textit{DP} sets for decoders and encoders are calculated separately as they vary at different scales. Based on Equation \ref{eq:dp-gt}, we do not need to {\fontfamily{pcr}\selectfont Pull} all tensors but each client decides what to share with $\mathcal{S}$ (only highly-fluctuating tensors in this case). Figure \ref{fig:DP} visualizes this concept.
\begin{figure}[htp]
    \centering
    \includegraphics[width=0.4\textwidth]{fl-update}
    \caption{DP-based communication in our FL setting.}
    \label{fig:DP}
\end{figure}

As shown in the figure, $C_1$ computes the difference between tensors from rounds $r$ and $r-1$ and decides to only share two tensors (vertices in diagrams) that have the highest norms. In this scenario, the communication cost between $C_1$ and $\mathcal{S}$ is approximately reduced by $78$\% for the pulling phase as only $2$ out of $9$ tensors ($22$\%) are transferred. The exact percentage of the bandwidth saved in this communication protocol directly depends on the client's architecture and $\theta$, but the figure uses an imaginary scenario to explain the reduction mechanism.

The intuition behind $DP_{g}^{c}$ is that only highly-fluctuating tensors should be involved in the communication process. It assumes that the main \textit{adaptation} (or \textit{out-domain}) knowledge lies in those tensors and what each client needs to learn about its external world is only communicated through such active tensors. Therefore, clients only need to exchange them with the server. This is an assumption that might either result in effective communication or conversely hurt the client. Because, if by any chance \textit{local} (or \textit{in-domain}) knowledge is stored in such tensors, $DP_{g}^{c}$ manipulates the most important parameters and overwrites them with external domains' information. In other words, there is a possibility that the reason for observing high fluctuations in active tensors is not because they carry the community knowledge but because they are responsible to learn local data, so they have to vary frequently to adjust and learn local data.

If this second assumption is correct, modifying the active set can easily delay the convergence of local models, and thus deteriorate their quality. Due to this concern, we propose another alternative, $DP_{l}^{c}$, which relies on less active tensors for communications. The pulling condition for $DP_{l}^{c}$ is as in Equation \ref{eq:dp-lt}:
\begin{equation} \label{eq:dp-lt}
\norm{w_r^t - w_{r-1}^t} < \theta
\end{equation}
which means, unlike the previous case, highly-active tensors are protected from external updates and only modified using local data. The less active tensors are assumed to be the representatives of external domains, so they are shared with the server and co-trained with other tensors. Both types of tensors contribute to the local training process, but this time the less active group is responsible for bringing the community knowledge to the client. In the next section, we compare $DP_{l}^{c}$ and $DP_{g}^{c}$ and show which strategy is more impactful.

\section{Experimental Study}\label{exp}
\begin{table*}[h]
\begin{center}
\begin{tabular}{ l  l l  l l  l}
\toprule
\textbf{Corpus} & \multicolumn{2}{c}{\textbf{Source (De)}} & \multicolumn{2}{c}{\textbf{Target (En)}} & \textbf{Sentences}\\
& Words & Tokens & Words & Tokens & \\\midrule
 \textit{WMT} & 119,920,225 & 1,529,872  & 126,731,132 & 691,150 & 4,468,841\\
 \textit{OS} & 33,502,036 & 339,627 &  37,373,751 & 174,670 & 4,500,000\\
 \textit{TED} & 2,710,904 & 99,221 & 2,861,006 & 45,364 & 143,837\\
 \textit{PHP} & 322,546 & 13,001 & 318,788 & 8,521 & 39,708\\
 \textit{UB} & 96,355 & 13,515 & 90,839 & 9,234 & 13,246\\\bottomrule
\end{tabular}
\caption{\label{tab:stat} Statistics of the training datasets. The second and third columns show the number of all words and unique tokens for the source and target languages, respectively, and the last column is the number of parallel sentences. \textit{OS} is a large collection, so we randomly selected a subset of it for our experiments. As the statistics show, we have different sets with different sizes from different domains which helps us have a fair and realistic simulation.}
\end{center}
\end{table*}

\subsection{Hyper-Parameters and Datasets}
Since our main baseline is the model proposed in \citet{Roosta2021CommunicationEfficientFL}, we follow their setting in the interest of fair comparisons. For our  translation engines, we use Transformers with their \textit{base} configuration \citep{vaswani2017attention}. Encoders and decoders have six layers (each), attention modules have eight heads, word embeddings and internal projection layers are $512$-dimensional vectors, and the inner layer in the position-wise feed-forward module has $2024$ dimensions. All other hyper-parameters/variables such as the training algorithm and scheduling are the same as the \textit{base} configuration unless they are clearly indicated in the paper. We used four NVIDIA V100 GPUs for all experiments.

Similar to \citet{Roosta2021CommunicationEfficientFL}, we work on the the German$\rightarrow$English direction. To train/test the models, we use five datasets of \textit{WMT},\footnote{\url{http://statmt.org/wmt14/translation-task.html}} \textit{OpenSubtitle} or \textit{OS} \citep{lison-tiedemann-2016-opensubtitles2016}, \textit{PHP}, \textit{Ubuntu} or \textit{UB}, and \textit{TED} \citep{TIEDEMANN12.463} where all corpora are \textit{normalized} and \textit{tokenized} with the scripts provided by Moses.\footnote{\url{https://github.com/moses-smt/mosesdecoder}} Table \ref{tab:stat} provides the statistics of the training datasets.

For the test and development sets of the \textit{WMT} model, we use \textit{newstest-14} and \textit{newstest-13}, respectively. For others, we randomly select 4,000 sentences: 2,000 for the test and 2,000 for the development sets.\footnote{The same sets used in \citet{Roosta2021CommunicationEfficientFL}} We also pre-processed datasets to segment words into sub-words by BPE \citep{sennrich-etal-2016-neural}. This helps create a shared vocabulary for source and target languages of all models and avoid out-of-vocabulary entries. Our BPE setting extracts $30$K unique tokens for each of the source and target sides.

One critical hyper-parameter in our model is $\theta$. Considering the selection criterion in Equation \ref{eq:dp-gt}, a small value of $\theta$ allows the majority of tensors to be transferred and hence leads to a minimal reduction in bandwidth. A very large value is also not plausible as it filters lots of tensors and prevents the client from receiving external knowledge. One solution is to run an exhaustive search to find the best value, which clearly is an expensive process and sometimes impossible in the case of FL. Vacillating between different options to set up $\theta$ requires the engagement of both client and server and could in fact be more costly than simply pulling all tensors. To cope with this and also make our results easily reproducible, we simply consider the median of the differences for $\theta$, meaning we only transfer \textit{half} ($50\%$) of the tensors and ignore the rest. The selection criterion determines which half. In $DP_{g}^{c}$, we consider the active half and in $DP_{l}^{c}$ the less active half is exchanged. Our results show that this simple strategy leads to effective communication without compromising much on quality.

\subsection{Centralized Model Training}
Our baseline results are summarized in Table \ref{tab:base}. The evaluation metric used in all experiments is BLEU \citep{papineni2002bleu} computed by SacreBLEU \citep{post-2018-call}.\footnote{\url{https://github.com/mjpost/sacrebleu}} As expected, models work accurately on in-domain data but perform poorly on other domains, specially if the domain is significantly different from  training samples, e.g. the \textit{PHP} model's BLEU score is \textit{zero} when translating \textit{WMT} test sentences.
\begin{table}[th]
\begin{center}
\begin{tabular}{l c c c c c}
\toprule
&\textit{WMT} & \textit{OS} & \textit{TED} & \textit{PHP} & \textit{UB} \\\midrule
 \textit{WMT} & \textbf{33.66} & 18.57 & 29.22 & 8.04 & 12.41\\
 \textit{OS} & 13.66 & \textbf{23.58} & 24.22 & 7.84 & 13.83\\
 \textit{TED} & 12.09 & 13.59 & \textbf{29.32} & 6.67 & 10.15\\
 \textit{PHP} & 0.00 & 0.26 & 0.26 & \textbf{34.48} & 0.00\\
 \textit{UB} &  0.28 & 0.78 & 0.75 & 2.30 & \textbf{30.15}\\ \bottomrule
\end{tabular}
\caption{\label{tab:base} Baseline results for the De$\rightarrow$En direction. Models are trained for $100$K steps. The first column indicates which training set is used to train the engine and other columns show models' performance on different test sets, e.g. [\textit{OS}][\textit{TED}] = 24.22 indicates the BLEU score of a model trained on the \textit{OS} training set and tested on the \textit{TED} test set. The best BLEU score for each test set is bold-faced.}
\end{center}
\end{table}

In order to remedy the poor quality for out-domain data, we train a central model and adapt it to all other domains with two techniques of \textit{data combination} and \textit{chained fine-tuning}. In \textit{data combination}, we simply concatenate all corpora to create a much larger training set. We initialize the central model with \textit{WMT} parameters and retrain it for extra $50$K steps with the new dataset. In \textit{chained fine-tuning}, we do not combine datasets but instead fine-tune the central model sequentially using each domain's training set for additional $50$K steps ($10$K for each), i.e. we start from the \textit{WMT} model, then sequentially fine-tune it over \textit{UB}, \textit{OS}, and other datasets one after another. This sort of fine-tuning could suffer from catastrophic forgetting, so we ran different experiments to figure out the best order of fine-tuning.

The \textit{chained fine-tuning} strategy could provide relevant baselines for FL experiments. Imagine a scenario where the central model is shipped to a client environment and it is updated there with multiple local datasets. In \textit{data combination}, we assume that all data is accessible at training time (a fully-observable environment with full access to all domains' data) whereas \textit{chained fine-tuning} pictures a more realistic scenario by forcing to update the central model gradually on the client side.

Table \ref{tab:dc} summarizes results for these two fine-tuning methods. Exposing the central model to other domains' data yields much better quality. \textit{Data combination} clearly outperforms and it shows the impact of having direct access to data; the privilege that we do not have in settings such as \textit{chained training} and FL.
\begin{table}[th]
\begin{center}
\begin{tabular}{c p{6.5mm} p{6.5mm} p{6.5mm} p{6.5mm} p{6.5mm}}
\toprule
Technique & \textit{WMT} & \textit{OS} & \textit{TED} & \textit{PHP} & \textit{UB}\\\midrule
\textit{chained}  & 18.26 & \textbf{23.51} & 28.19 & 16.14 & 23.05 \\
\textit{combination} & \textbf{33.50}& 21.82 & \textbf{31.51} &  \textbf{37.56} & \textbf{35.61} \\ \bottomrule
\end{tabular}
\caption{\label{tab:dc} Domain adaptation results in centralized settings.}
\end{center}
\end{table}
\subsection{Federated Learning Results}
Models reported in the previous section (specially in Table \ref{tab:dc}) are high-quality engines that are trained in a centralized fashion and provide acceptable performance for all domains. Fine-tuning addressed the problem of poor quality for out-domain data, but as  discussed previously, centralized fine-tuning and having access to out-domain data might not be always possible. Therefor, in this section we try to train comparable alternatives in an FL setting.

Our setting has one server and five clients (one for each dataset). In the interest of fair comparison between the FL and centralized approaches, we initialize all the clients with \textit{WMT} parameters. Each client updates its model with local data and shares it with the server in each round. We {\fontfamily{pcr}\selectfont Pull} client updates after $10$K steps of fine-tuning for aggregation and repeat this process for $5$ rounds. In total, each model is fine-tuned for $50$K steps which is identical to the setting we used for centralized training. Results for this experiment are summarized in Table \ref{tab:fl}.
\begin{table}[th]
\begin{center}
\begin{tabular}{c p{6.5mm} p{6.5mm} p{6.5mm} p{6.5mm} p{6.5mm}}
\toprule
& \textit{WMT} & \textit{OS} & \textit{TED} & \textit{PHP} & \textit{UB} \\\midrule
$\mathcal{S}$ & \textbf{33.97} & 19.17 & 30.8 & 37.32 & 47.9 \\
 \textit{WMT}$^c$ & {32.07} & 18.28 & 29.55 & 9.55 & 13.75 \\
 \textit{OS}$^c$ & 19.05 & \textbf{23.39} & 27.85 & 13.57 & 18.58 \\
 \textit{TED}$^c$ & 17.37 & 16.05 & \textbf{34.30} & 11.83 & 17.05 \\
 \textit{PHP}$^c$ & 4.07 & 4.33 & 7.48 & \textbf{45.07} & 10.90 \\
 \textit{UB}$^c$ & 0.77 & 4.27 & 5.66 & 14.98 & \textbf{49.51} \\ \bottomrule
\end{tabular}
\caption{\label{tab:fl} FL results with $1$ server ($\mathcal{S}$) and $5$ clients (indicated with the $c$ superscript), e.g. \textit{OS}$^c$ is a client initialized with WMT parameters and updated with its own data (\textit{OS} training samples).}
\end{center}
\end{table}

The first row in Table \ref{tab:fl} belongs to the server. FL affects model training quite positively and provides significantly better BLEU scores, specially for those low-performing models such as \textit{PHP}. The average BLEU score of the server over different domains is $33.83$, which is \textbf{1.83} points higher than that of the best model reported in Table \ref{tab:dc}. This means, even though FL does not access clients' data, it is more impactful in fusing information and training mixed-domain engines. This outcome for a complex task such as NMT was unexpected.

After the final FL round, the server parameters are pushed back to clients so they can also benefit from the result of aggregation. At this point, each client \textit{can} decide to run another phase of fine-tuning with local data over the server parameters. This is a trade-off between being domain specific and remaining generic. Results for this process are listed from the second to last rows in Table \ref{tab:fl}, e.g. \textit{PHP}$^{c}$ after the last {\fontfamily{pcr}\selectfont Push} step has access to all server parameters so its BLEU score on in-domain data is $37.32$, similar to that of the server. It is also able to translate other domains with the average BLEU score of $32.96$. However, as the fifth row shows, if it decides to fine-tune the last parameter set it received from the server with its own local data, its BLEU score increases from $37.32$ to $45.07$ on the in-domain test set, but at the same time it loses its generalization over other domains

We also ran an ablation study to evaluate how the number of FL rounds impact model quality. Two important factors in FL settings are the number of clients and training rounds. We can pass over the first one as we have a cross-silo setting with a limited number of clients, but Table \ref{tab:five-ten} and Figure \ref{fig:num-client} provide additional information on the second hyper-parameter.
\begin{table}[th]
\begin{center}
\begin{tabular}{c p{6.5mm} p{6.5mm} p{6.5mm} p{6.5mm} p{6.5mm}}
\toprule
& \textit{WMT} & \textit{OS} & \textit{TED} & \textit{PHP} & \textit{UB}\\\midrule
$\mathcal{S}_{5}$ & \textbf{33.97} & 19.17 & 30.8 & 37.32 & 47.9\\
$\mathcal{S}_{10}$ & 31.90 & 19.63 & 31.04 & 38.33 & 48.63\\
$\mathcal{S}_{50}$ & 31.05 & \textbf{20.83} & \textbf{32.27} & \textbf{43.99} & \textbf{51.07}\\\bottomrule
\end{tabular}
\caption{\label{tab:five-ten} FL results for \textit{rounds} $\in\{5,10,50\}$. \textit{rounds} = $50$ means aggregation occurs $50$ times between checkpoints $100$K (where FL training starts) and $150$K (where FL training ends).}
\end{center}
\end{table}
\begin{figure}[htp]
    \centering
    \includegraphics[width=0.4\textwidth]{fl-rounds}
    \caption{The learning curve of the \textit{UB} model for \textit{rounds} $\in\{5,10,50\}$.}
    \label{fig:num-client}
\end{figure}

Results from the figure/table above show that the number of rounds and model performance increase proportionally in low-quality clients such as \textit{UB} or \textit{PHP}. This was expected since with a higher number of rounds clients are updated more frequently with rich information from the central server. However, it comes at a price as it increases communication load. It also delays local model training, because in each round the client has to suspend training to read server values and updates its internal model. For other high-quality clients such as \textit{WMT}, higher rounds lead to some degradation since they receive external updates from less-accurate peers and have to compensate for their low quality. The choice of the number of rounds is a trade-off between quality and bandwidth.

\subsection{Dynamic Pulling Results}
We proposed a novel technique for better pulling and mentioned that it is able to reduce the communication load yet maintain model quality. Table \ref{tab:low} reports related results to support our claims.
\begin{table}[th]
\begin{center}
\begin{tabular}{c p{6.5mm} p{6.5mm} p{6.5mm} p{6.5mm} p{6.5mm}}
\toprule
& \textit{WMT} & \textit{OS} & \textit{TED} & \textit{PHP} & \textit{UB}\\\midrule
\textit{default} & 33.97 & 19.17 & 30.8 & 37.32 & 47.90\\\hline
$DP_{l}^{c}$ & 29.28 & \textbf{19.17} & \textbf{30.88} & \textbf{36.33} & \textbf{45.74}\\
$DP_{g}^{c}$ & \textbf{30.74} & 18.28 & 27.61 & 13.69 & 32.16\\
\textit{random} & 24.58 & {19.16} & 30.57 & 35.33 & 42.50\\\bottomrule
\end{tabular}
\caption{\label{tab:low} The impact of different communication techniques on model quality. The first row is copied from Table \ref{tab:fl} for easier comparison. The bold-faced numbers are the best results obtained by DP-based techniques.}
\end{center}
\end{table}

The average BLEU scores for the \textit{default}, $DP_{l}^{c}$, and $DP_{g}^{c}$ methods on different domains are $33.83$, $32.28$, and $24.49$, respectively. This means the assumption that less active tensors are responsible for domain adaption could be true and highly-fluctuating tensors should only be kept for learning in-domain knowledge. We also provide results from our \textit{random} configuration, in which we exchange the same number of parameters as in $DP_{l}^{c}$ and $DP_{g}^{c}$ but those parameters are selected randomly. The comparison between \textit{random} and other alternatives shows that the selection criterion directly affects model quality.

Although there is a gap by $1.55$ points between \textit{default} and $DP_{l}^{c}$ (which is meaningful in NMT), $DP_{l}^{c}$ could still be a strong candidate when training large models in the context of FL, because the number of parameters exchanged in each pulling step is \textit{45,724,160} for \textit{default} whereas this number is only \textit{22,863,104} ($50$\% less) for $DP_{l}^{c}$. It should also be noted that pulling occurs not once but for multiple rounds and saving $50$\% each time is a significant gain. Moreover, $DP_{l}^{c}$ still performs on par with \textit{data combination} which is a strong but centralized and not private baseline

\subsection{Comparison to Controllers}
As previously discussed in Section \ref{da}, Controllers \citep{Roosta2021CommunicationEfficientFL} only exchange 4 layers (2 from the encoder and 2 from the decoder in a 12-layer Transformer) with the server in the {\fontfamily{pcr}\selectfont Push} and {\fontfamily{pcr}\selectfont Pull} phases, which means the communication bandwidth they reduce is around $66\%$ ($\frac{4}{12} \approx 33\% $ of the layers are only exchanged between the server and clients). In our case, \textit{DP} only affects {\fontfamily{pcr}\selectfont Pull} which leads to $25$\% bandwidth reduction in client-to-server exchanges.\footnote{{\fontfamily{pcr}\selectfont Pull} is only $50\%$ of the communication where we excluding half the parameters using the threshold, so $50\% \times 50\% = 25\%$.}

We challenged our model to see if we can also save $66\%$ in bandwidth by sending/receiving the same number of parameters as in Controllers. More precisely, we applied our threshold-based strategy in \textit{both} {\fontfamily{pcr}\selectfont Push} and {\fontfamily{pcr}\selectfont Pull} and modified the value of the threshold such that it only accepts $33\% $ of the parameters to exchange with the server. Table \ref{tab:controllers} summarizes results of this experiment.
\begin{table}[th]
\begin{center}
\begin{tabular}{c p{6.5mm} p{6.5mm} p{6.5mm} p{6.5mm} p{6.5mm}}
\toprule
& \textit{WMT} & \textit{OS} & \textit{TED} & \textit{PHP} & \textit{UB}\\\midrule
DP$^{c}_{l}$ & 29.52 & 19.25 & \textbf{31.53}& \textbf{36.19}&\textbf{45.40}\\
DP$^{c}_{g}$ & 29.09 & 19.07 & 30.93 & 34.47 & 41.57\\
6E6D (0-3) & 31.13	& 19.19& 	30.95&	33.79&	32.85\\
8E8D (0-6)	& \textbf{31.79}	& \textbf{20.02}	&30.6	&32.43	&33.41
\\\bottomrule
\end{tabular}
\caption{\label{tab:controllers} Dynamic parameter selection versus Controllers.}
\end{center}
\end{table}

In our comparison, we selected the two best performing Controller models reported in \citet{Roosta2021CommunicationEfficientFL}. The \textit{6E6D (0-3)} configuration is a Transformer with a 6-layer encoder and 6-layer decoder whose first and fourth layers are selected to act as Controllers. In the \textit{8E8D (0-6)} configuration, instead of using the original encoder/decode layers as Controllers four additional layers (two for the encoder and two for the decoder) are defined which are placed after layers 0 and 6. This means, in an 8-layer encoder/decoder the first and seventh layers are Controllers and the rest are ordinary layers.

Results show that our model could be a reliable alternative for communication-efficient FL, even though we aggressively limited the number of parameters exchanged in this new setting. Moreover, in our model we do not need to define additional layers. Unlike Controllers, we also do not have to deal with finding the correct position to place Controller layers. According to \citet{Roosta2021CommunicationEfficientFL}, the final model performance is directly impacted by misplacing Controllers and our solution solves that problem.

\section{Conclusion and Future Work}\label{conc}
In this paper, we reported a set of benchmarking result for NMT in the context of FL. We also proposed an effective technique to reduce the communication bandwidth. Our solution tries to determine a subset of parameters that are responsible for learning out-domain knowledge and only exchanges them with the server.

In future work, we are interested in \textit{i}) adding more languages to train multilingual engines, \textit{ii}) improving communication protocols even further, \textit{iii}) comparing other FL algorithms in the presence of \textit{DP}, and \textit{iv}) investigating NMT in cross-device settings.

\section*{Acknowledgments}
We would like to thank our anonymous reviewers for their valuable comments as well as Liling Tan, Grant Strimel, and Sriram Venkatapathy (from Amazon) for providing feedback on the first version of this paper.

redpajama_set_name: RedPajamaArXiv


text:
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Home » USA Online Casino Blog » How to Bet on the 2018 World Cup
USA Online Casino Blog
How to Bet on the 2018 World Cup
Angeline Everett
One of the largest sporting events in the world will take center stage in 2018. The FIFA World Cup, which will be held in Russia, begins on June 14. And, while national pride is certainly at stake, bettors from around the globe will also be cashing in on all kinds of wagers from game bets to futures and props.
This year's World Cup will be the 21st of its kind and will have no less than 32 teams participating, including 20 that competed in the 2014's World Cup.
The Most Promising Teams
Because Germany came out on top four years ago, it isn't too surprising that, right now, Germany is touted as being one of the co-favorites to be the outright tournament winner. Brazil is the other. Germany has high hopes of joining Brazil and Italy as countries that have won back-to-back World Cups. However, the last time there was a repeat World Cup winner, it was in 1958 and 1962, and the country was Brazil.
There are two teams that are playing in the tournament for the very first time – Panama and Iceland. Because both teams are newcomers, they are longshots. However, there are plenty of other well-known soccer powerhouses to choose from, such as Argentina, Spain, and France, all of which have been previous winners.
Russia, on the other hand, is hoping to benefit from playing on home soil. The country is hoping to join the other six host nations that have previously won the coveted title. The last time this happened was in 1998 in France, when France became the host nation to win. Although originally the odds on Russia to win were poor at +3,300, after the group draw, the odds now stand at +120 to win its group.
Of course, not all the bets will be on the tournament's outcome. There will be a plethora of other wagers that can be made, too, including prop bets on individual players, such as the Golden Boot (awarded to the highest goal scorer in the tournament) and the Golden Ball (awarded to the best overall player).
The Groups
When the groups were drawn, the teams came out as follows:
A: Russia, Egypt, Saudi Arabia, and Uruguay
B: Portugal, Morocco, Spain, and Iran
C: France, Peru, Australia, and Denmark
D: Argentina, Croatia, Iceland, and Nigeria
E: Brazil, Serbia, Costa Rica, and Switzerland
F: Germany, South Korea, Sweden, and Mexico
G: Belgium, Tunisia, Panama, and England
H: Poland, Colombia, Senegal, and Japan
Odds Offered Post-Draw
Since the draw was made, the following odds are now being offered:
5/1 Brazil and Germany – joint favorites
6/1: France
8/1: Argentina
15/2: Spain
12/1: Belgium
16/1: England
25/1: Colombia, Portugal
28/1: Croatia
33/1 Uruguay, Russia
The Process of Betting
If you have never bet on the World Cup before, here are some simple steps to follow to make sure you place your first wager on this sporting event properly.
Having a good understanding of how this tournament works is vital. The very first FIFA World Cup was held in 1930 in Uruguay. Since then, it has been held every four years. The country that will host the event is selected in advance and is always granted a spot automatically in the lineup. The teams from the other countries must participate in qualifying tournaments in their region over the two years that precede the finals. Eventually, 32 teams will earn a place in the tournament.
At this stage, the 32 teams are divided up into eight groups of four teams each, and a group stage is played. The 16 winning teams then go on to a knockout stage. After that, all games are "sudden death," and the winner of each will go on to the following round, with the loser being out of the competition. The quarterfinal sees eight teams playing; the semifinals is down to just four teams. Then, in the final, the two remaining teams battle it out for the title.
Understanding Betting on Soccer
There are several ways in which you can bet on soccer. The most basic wager is a bet on which team will win or lose a match. Some other bets include wagering on the first player to score a goal, or on the match's final score. The most lucrative wagers are on multiple outcomes, or multiplier bets, such as the final match score and the first player to score.
Understanding Betting on The World Cup
There are even more options open to bettors who are enthusiastic to place a wager on the World Cup. Of course, you can bet on which team will be the outright winner at the end of the entire tournament. But, you could also choose to bet on which team will win all the first eight groups, or on the outcome of a single match. Overall, there are more than 100 different betting markets for every game, even exotic wagers, such as betting on the top scorer for the entire tournament.
Knowing the Rewards and Risks
Every bet has risks, and it goes without saying that the odds-on offer will indicate your chances of winning. Yes, the odds on an outsider being the outright winner may be attractive, but is it likely to happen?
Deciding A Budget
You need to know how much you're prepared to potentially lose before wagering on the World Cup. The excitement can lead to your getting carried away. So, imposing a realistic budget is paramount. Decide your entire budget for wagering for the whole tournament and, then, allot amounts carefully to every stage so you don't blow it all at once. If you do have a win, you might be tempted to put your winnings into further bets. This is often a bad idea, however, since you could lose all your gains.
Look for Betting Tips
You need to stay up to date with the news about the tournament. Insider information, such as a player injury, could hugely influence the bets you make. Read blogs, news articles, and listen to podcasts so you can be well-informed.
Choose A Reliable Sportsbook
You need to find a reliable sportsbook to place your wagers. Try to choose one that offers plenty of options and that has a good international reputation for your own peace of mind.
Place Your Bet
Once you've chosen a bookmaker, you can complete your betting slip, put down your money, and you're ready to go. Most online sites have an online help center, so you can get assistance if you require it.
Check the Results and Collect Any Winnings
If you're lucky and win your bet, you can enjoy the best part of the process – collecting your money! As matches take place frequently during this event, and there is plenty of coverage in the media. You'll find it easy to check scores, so you'll know when you need to collect. If you're betting online, you'll find your winnings deposited automatically into your account.
Make Sensible Bets
Although supporting your own national team is always fun, it won't always be a sensible choice when it comes to betting, especially if you're wagering large amounts. Be sure to use your common sense, as well as your loyalty, before you put down a huge bet.
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A Massachusetts native, blogger Angeline Everett grew up in the Allston neighborhood of Boston and earned a degree in casino management from the University of Massachusetts-Amherst. After graduating, Angeline moved to Atlantic City where she joined the young team at the Borgata Casino as a compliance representative, while blogging on the side. After a few years in the back office, Angeline moved to the floor to work first at a casual poker dealer and later casual poker floor supervisor. Fascinated with games of chance since she was a child, Angeline currently divides her time between blogging and work on her first book.
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text:
https://mmajunkie.usatoday.com/2017/04/bellator-177s-leandro-higo-has-no-doubt-hell-beat-champ-dantas-expects-to-see-fans-on-their-feet
Bellator 177's Leandro Higo has 'no doubt' he'll beat champ Dantas, expects to see fans 'on their feet'
Fernanda Prates and Christian Stein
April 12, 2017 4:15 pm ET
Title challenger Leandro Higo is in agreement with champ Eduardo Dantas when he says Friday's Bellator 177 headliner has all the potential to be a barn burner.
But, as expected, he and Dantas (19-4 MMA, 9-1 BMMA) don't really see eye to eye when it comes to the belt's destination after it's all said and done. While he's the promotional newcomer taking on the defending, two-time champ, Higo (17-2 MMA, 0-0 BMMA) has all the confidence that he'll make the most out of his big break.
"I'm very happy to debut in Bellator against Dantas, who's one of the best fighters in our weight class," Higo told MMAjunkie ahead of the Spike-televised main event at Budapest Sports Arena in Budapest, Hungary.
"I'm putting on a show and bringing the belt home – no doubt."
Higo's trajectory has had its share of ups and downs. Not only did a string of cancellations keep him sidelined from professional competition for almost two years, his "Ultimate Fighter Brazil: 4" run turned out to be a frustrating one. The adversity, however, worked to make him "the fighter that he is today." And he's since bounced back in style, going on an undefeated four-fight, three-finish streak.
Higo's most recent effort, a unanimous call over Steven Peterson, earned him LFA's inaugural 135-pound belt and eighth professional win in a row. A few months later, he'd be getting the call to join the big leagues. And while the shot was, in the Brazilian bantamweight's opinion, way overdue, it also arrived during his "best phase."
Sure, coming in on short-notice is never the ideal preparation scenario. But, having stayed ready exactly so he would be able to answer to calls like these, Higo finds himself in good shape to try and take the 135-pound belt.
"It's not an adequate amount of preparation time, but that's why I'm always training," Higo said. "Sometimes, an opportunity might materialize, and the fighter is not ready. So I always keep that in mind. Life is full of opportunities, and I've been able to make it this far by seizing those that came my way.
"I'm training as much as I can. I'll be looking to catch (Dantas) at the right moment, when he gives me an opening. I'll fight intelligently. I can't go easy on him."
Higo's caution stems from what seems like a lot of respect for "Dudu" Dantas' ever-evolving game. Since first losing the Bellator belt to Joe Warren in 2014, the Brazilian champ returned with a vengeance, going on to reclaim the belt and defend it in equally dominant fashion on a rematch with Warren.
Higo has been following Dantas' career and is, therefore, highly aware of what he brings to the table. But that doesn't mean he's in any way intimidated by it.
"Eduardo Dantas is very versatile," High said. "He comes from jiu-jitsu, as do I. He has very good striking. He uses his reach very well. He's tall for our weight class. He's been doing very well, always controlling the distance against his opponents. He has shown great wrestling improvements. As it is, many of our potential opponents in Bellator are wrestlers.
"So we have to keep that tool especially sharp. He doesn't shy away from a fight, and moves very well. I'll be doing the same. All the elements are in place for us to put on a show for the fans. I've been studying him for a few years. I always study all belt holders. This isn't a game for me. And just like on my last fight, the fans will be on their feet."
Higo is so confident, in fact, that a title win on his promotional debut is just a small part of an even more ambitious plan.
"Whoever wants to bet on me, go ahead," Higo said. "Victory is guaranteed.
"After this fight, I want to be very active. I'd like to defend my belt as frequently as possible. I want to carve my own history in the promotion, as my training partners Patricio and Patricky Freire have done."
For more on Bellator 177, check out the MMA Rumors section of the site.
Ryan Bader enters Bellator 273 as betting underdog vs. Valentin Moldavsky
Bellator heavyweight champion Ryan Bader is in serious danger of losing his title this weekend – at least according to oddsmakers.
Video: What's the most anticipated fight of the weekend?
Our "Spinning Back Clique" takes a look ahead to determine what is a must-see bout from this weekend's slate of fights.
More Bellator


was two notes on a string.
[SOUND] And up and down.
Now the next one I just wanna add a note.
scale and two notes there.
that's a nice E minor triad sound.
pinky, for that note on the G string.
two with a pentatonic scale.
You can hear how the rhythm is the same.
so we've already done this.
the reason it's not difficult.
Is because you have nice pull-offs.
All right, so let's check this out.
[SOUND] All right, I've got it.
[SOUND] We're familiar with that.
[SOUND] On that G note.
And we'll follow that with a hammer-on.
[SOUND] And then one more up stroke here.
this note [SOUND] in the middle.
I'm gonna do it with a space in between.
with a hammer under the middle.
that we're not playing are ringing out.
see if we can control those other notes.
note my finger's touching real lightly.
Mutes it out a bit, let's try that again.
[SOUND] Make sure those notes are clean.
[SOUND] With a hammer in the middle.
our ear onto that lick.
So we got a good triplet lick here.
three, one two three, one two three.
down, up, up, down, down, up, up.
I like to practice these with stops.
sounding what's going on here?
groove and we're going to stop at the end.
and we'll work our way up to there.
That way I can really listen to the notes.
I can adjust my technique.
To make sure that every note is strong.
pull-offs that are in there.
My stop note is right on the beat.
I can hit it with the foot.
I know right where it is I'm in control.
Let's see how fast we can get it.
it's done exactly what I wanted it to.
do to make it sound good.
harder with my left hand.
the details of the lick.
So when we play it fast.
really pick out the details of that lick.
a very specific system of picking with.
up, up, making sort of a shuffle.
hammer-ons and pull-offs are strong.
we have ourselves pretty cooking lick.


How do I breed horses?
If you own a mare you can breed it with another stallion of your own or with another player's stallion who are currently being offered for stud at a price.
3. Select the "Breed" button and you will be taken to the Stud farm where you can select one of your own stallions or another players stallion to stud with.
If you are not sure whether a horse is of good breeding stock have a look at how many races it has run and how many wins/places/shows it has, that is an good indicator of good horse genes! The pedigree rating is also a good indicator and takes into account a broad range of factors including trainability, maximum stats as well as inherited strength and endurance.
The error message says I don't have room for the foal?
If you are getting the message that you have no room for the foal, it means that either you have a full stable (15 horses) or you need to expand your stables to accomodate the new foal. See here for more information.
How do I become a stud breeder?
Firstly you must purchase a stallion. Once the horse is 2 days+ old you have the option send it to stud. Select the horse in the stable and select the "Stud" button. Set a price within the range offered and when another player selects your horse to stud with, you will receive the stud fee.
There is a 12 hour limit on the amount of time the horses can stay in the stud farm without being selected. If your horse has returned to the stable and there is nothing listed in your recent activity list then your horse did not get chosen to stud in the timeframe. Otherwise it will be listed in the "Activity" tab when selecting "View My Profile".
How can I improve my horse's chances of being selected at the Stud farm?
If you are a VIP, you can purchase a DNA certificate from the Science lab which will display your horses stats to a prospective buyer. Players generally like to breed with horses with good track form as well as 'good genetics'.
When is the stud fee paid?
The stud fee is paid at the time of breeding (i.e. when you select the horse from the stud farm).
Why are my horses offspring not as good or better than the sire/mare?
There are two reasons why the breeding can appear to go 'backwards' in strength or endurance.
2. If the average of the two breeding horses results in a low number. Say the average of the two breeding horses is just 15.1 stars in strength. Then, for example, in breeding the foal there is growth of .8 in endurance but -.2 in strength. The foal's strength will show at 14 stars, but the horse has actually experienced growth.
At what age should you breed your horses?
The pedigree of the horse is based on the stats of the horse at birth. Training or racing the horse does not affect its stats. So breeding will be the same at any time regardless of age, but obviously, the earlier you start, the more offspring you can breed.
My mare has died while expecting – will the foal still arrive?
Yes, the stable will remain 'reserved' and the foal will still arrive on schedule.
Can you breed horses that you have put on steroids?
Yes, you can. You just can't sell steroided horses.
Can I change a horses name ?
Hi Joe, yes, you can but it will cost 1 token to do so.
How? I would like to communicate with others but it seems looking up their FB profiles and asking a FR is the way but is there an internal within the site manner to add/chat with friends?
Why is the chat screen not active for me?
When you first played the game you selected to disable chat in the game. To reset the chat question visit this link http://www.hoovesoffire.com/hooves/reset-restriction.php in your browser and then load up the game again.


Listen closely to this one, as it's very easy to get lost while defending the second premise of the Kalam. Fortunately for you, Tim takes you through exactly what you need to know for 95% of all of your interactions using this argument.
This premise of the Kalam is a tough one for me to defend, as I am not very good with a lot of the science that is talked about in support of it. But what I love about this argument is that, we, as Christians, are arguing in the same way as the mainstream scientific community! Science is not our enemy! It is so useful in defending premises in arguments that have theological implications in their conclusions.
I know this is a long episode but be sure to listen as many times as you need in order gain a basic understanding of the argument. Even if you don't understand a lot of the science & philosophy talked about in this episode, don't be discouraged. We all had to start somewhere.
Make sure to leave a rating & review on iTunes and keep sharing the show! We really appreciate the support.


text:
Q: Powershell script to batch update multiple git repos randomly asks for login I wrote a powershell script to update multiple git repositories.
Everything is working fine, except for that on seemingly random times, the script suddenly asks for login credentials.
Does anyone know what could cause this?
Just to make sure, it does not always ask for login information. It works just fine, and updates all repos. It just randomly stops on one (random) repo, and asks for the login. After that it works fine again for a while.
I'm thinking about maybe building in a "sleep" in between repo updates, as the script might be requesting the updates to fast in a row, could this cause it?
Kind regards,
Michael

A: This issue is indeed cause by performing git calls to quick after another, by adding a delay in between git calls, the issue is gone totally.
So to anyone who wants to use a script to update a whole lot of repositories sequentially, sleep 1 second in between git calls, and you should not have this issue.

redpajama_set_name: RedPajamaStackExchange


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Prince Royal (Portuguese: Príncipe Real) was the title held by the heir apparent or the heir presumptive to the throne of the Kingdom of Portugal, from 1825 to 1910.

The eldest son of the Prince Royal held the title of Prince of Beira.

History
The title of Prince Royal (Príncipe Real) was created in 1815, when the prince regent D. João (future King João VI of Portugal) elevated the State of Brazil to the rank of a kingdom within the United Kingdom of Portugal, Brazil and the Algarves. At this moment the old title of Prince of Brazil was extinct.

With the deposition of the Portuguese monarchy, in 1910, and King Manuel II's death in 1932, the title became abeyant.

List of Princes Royal

See also
Prince of Portugal
Prince of Brazil
Prince of Beira
Infante
List of Portuguese monarchs

References

Heirs to the throne
Princes of Portugal
Portuguese monarchy
Princes Royal of Portugal

redpajama_set_name: RedPajamaWikipedia


Yesterday my friend Michael Meteyer and I visited the Kansai Guide dog Association near Kyoto, Japan.
Their dogs are related to my dog through breeding cooperation with Guiding Eyes for the Blind.
Thanks to Mr. Yoshi FUKUI, director of KGDA'S breeding program for giving us a tour of his lovely facilities.


Fargo Chiropractor | Nature, don't leave your healing up to it!
Every day we Fargo chiropractor is here someone say oh itll just fix itself. Let today be the day that we stop using this term to make no are the things that are bothering us. That ache in your neck or the next time you scratch your back give us a call because I guarantee you that we can relieve you at that pain much quicker than nature itself. Just like a car our bodies require me and the Cairo health and rehab are here to service you. Don't dwell in pain just waiting for it to go away when the key to living a life with out that bother send pain is so close within your grasp.
We believe that life does not have to be field with a bunch of terrible things and that a Fargo chiropractor can be one of the very dead things in your in your life. Our customer service is constantly improving because we now that there is always something we can be doing better in order to get you feeling better. From making sure the facility is in a state-of-the-art condition and keeping you at the forefront of our minds we guarantee that our customer service will not be beaten.
From a simple adjustment to rehabilitation that occurs once a week we can get you feeling like your young again. When you stand up to quick and your back screams for help reach out to this Fargo chiropractor so that we can help quiet those screams for you. We know that you are a very unique individual which is why we offer a wide range of services so that we can find the one that best suits you. We are not scared as new technologies or industry that often experiences change that we are aware that new does not always mean better. Which is why our services are and take intestate so that we can best serve you.
Advertisement is everywhere and are constantly people grabbing for your attention that don't take our word as we value our customers opinions much more than are out. Give our Google reviews the peak and you will quickly come to the conclusion that our customers add door Cairo health and rehab. Because our focus is on you there's a high value placed on how you are feeling from the moment you walk through our doors to the second that you leave.
No longer will be ignored the things that are holding us that we must seek out help and next time we throughout our back or wait With a crick in your neck. We do not live life in pain, that is why there people like I said dedicated our careers figuring out how to best service you. You keep a close eye on when your car should go into the shop so how about you keep a closer eye on how you feel and value yourself and not to give us a call when that pain occurs.


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Pretty Little Liars Is Leaving Netflix in July—and We're Blaming 'A'
12 Movies and TV Shows You'll Regret Not Watching This Week
Valentine's Day is this week, and, just to be clear, it doesn't really matter how you spend the holiday. Whether you're single or in a relationship is irrelevant, as long as you're living your best damn life. And the key to doing that, in my opinion, is by watching some A-plus movies and TV shows. Thankfully, there's a lot of good stuff airing this week to choose from. Check out the highlights, below, and enjoy Valentine's Day in any way you please (as long as it includes The Bachelor: Winter Games, of course, because it looks delicious).
Here and Now Holly Hunter stars in this new series about a multiracial family with three adopted children whose bonds are tested when one of the children starts having strange visions. We're certainly intrigued. 9:00 P.M. ET on HBO
Homeland Season seven of Showtime's addictive drama kicks off tonight. Remember, Carrie's officially left the White House and is living with her sister, Maggie, in Washington, D.C., in hopes of orchestrating the release of 200 people arrested under President Keane's administration. So there's a lot of stakes going into this premiere. 9:00 P.M. ET on Showtime
DC's Legends of Tomorrow CW's most eclectic DC-universe show returns from hiatus this evening, so set your DVRs for all the Firestorm goodness to come. 8:00 P.M. ET on the CW
Model Squad: Fashion Week Get a behind-the-scenes look at supermodels' lives during New York Fashion Week in this dishy new special. 10:00 P.M. ET on E!
The Bachelor Winter Games Bachelor favorites from both the international and American editions come together to find love and play some winter sports. Honestly, there's nothing more fun than watching hot people wipe out while skiing and then make out. 8:00 P.M. ET on ABC
Hailey Knox, "Don't Got One": The isolating effects of social media are explored in this gorgeous new video from pop's newest recruit, Hailey Knox. Watch the video, below:
He Lied About Everything This explosive documentary special unfolds the romance gone wrong between a news producer and a suave doctor that captured her in a web of lies. 8:00 P.M. ET on ID
The Top 14 Greatest Valentine's Day Movies of All Time Did your favorite make the list? Tune in to this special to find out, and get some ideas about what to watch. Maybe there's a movie on this list you haven't seen! 9:00 P.M. ET on The CW
Re:Mind, Season 1* Eleven high school classmates wake up restrained to a random dining table, and more horrifying events happen from there. This…is not for the faint of heart. Watch the trailer here. Streaming on Netflix
Everything Sucks! This sweet new comedy series takes place in the nineties and centers on members of an AV club and drama club navigating high school life. Streaming on Netflix
Black Panther If you see one new thing this week, make it Black Panther. This groundbreaking new superhero film is already breaking presale records and receiving acclaim from critics. Oh, and Lupita Nyong'o and Sterling K. Brown are in it. That's more than enough incentive to go to the theater. In theaters
The Catch, Season 1 This Shonda Rhimes series about about a private investigator who's swindled out of millions of dollars was short-lived, but it's nonetheless enjoyable. Watch the first season on Netflix before it leaves! Leaving Netflix on February 28
30 TV Shows, Movies (and More!) Gilmore Girls Fans Will Love
25 Movies You Need to Stop Feeling Guilty for Loving
Topicsmoviestvblack pantherwatch this
Lynsey Eidell
Friends Is Officially Leaving Netflix, and People Are Devastated
The Office Is Leaving Netflix—And People Are Freaking Out


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In Hartford, federal lawmakers pledge to hold HUD accountable for concentrating poverty in city neighborhoods, slum housing projects
Connecticut Delegation Bashes Trump's Pick Of Bannon As Chief Strategist
By Russell Blair
Stephen Bannon, campaign CEO for President-elect Donald Trump, leaves Trump Tower in New York on Nov. 11, 2016. (Evan Vucci / AP)
Members of Connecticut's all-Democratic congressional delegation slammed President-elect Donald Trump for choosing conservative news executive Steve Bannon as his chief strategist.
"Bannon has built his career peddling racist, sexist, and homophobic falsehoods," said Rep. Elizabeth Esty. "He is unfit to serve in any administration that values equality, diversity, peace, or even basic facts."
The executive chairman of Breitbart News, Bannon joined Trump's campaign in August after a series of staff shakeups. Critics say his website is a breeding ground for the types of conspiracy theories and hateful rhetoric that have fueled the so-called alt-right movement.
"This choice has left many Americans -- including a number of my constituents -- deeply disturbed and sincerely troubled," said Rep. John Larson. "In the words of the late Maya Angelou, 'When someone shows you who they are, believe them; the first time.' The content Bannon curated in his role at Breitbart News has more than shown who he is."
Members from Connecticut were among the more than 120 people who signed onto a letter written by Rep. David Cicilline, a Rhode Island Democrat, calling on Trump to reconsider the appointment.
"Since the election there have been a number of incidents across the country in which minorities, including Muslim Americans, African Americans, Hispanic Americans and Jewish Americans, have been the targets of violence, harassment and intimidation," the members wrote. "Mr. Bannon's appointment sends the wrong message to people who have engaged in these types of activities, indicating that they will not only be tolerated, but endorsed by your administration."
Elizabeth Esty
David Cicilline
John B. Larson
Latest Capitol Watch
Lawmakers set hearing on proposal to repeal state's religious vaccine exemption
Merrill wants voters to sign up for new text service
Manchester Man Charged With Home Improvement Scam In Four Towns


You are visiting the placeholder page for Sheila Armstrong. This page is here because someone used our placeholder utility to look for Sheila Armstrong. We created this page automatically in hopes Sheila Armstrong would find it. If you are not Sheila Armstrong, but are an alumni of Edinburg High School Edinburg, TX, register on this site for free now.


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A Teenage Girl Contracted Bubonic Plague In Oregon By….
30 Oct, 2015 by	Sierra Marlee
Like many undesirable things from years past, (i.e. the fedora and bell bottoms) the Bubonic Plague has made a comeback. A teenage girl in Oregon has fallen ill after a hunting trip where it is believed she was bitten by an infected flea.
From The Daily Mail:
A 16-year-old girl has contracted bubonic plague believed to be from an infected flea's bite during a hunting trip in Oregon.
The victim – who has not been identified – fell ill five days after she started the trip near Heppner, Morrow County, on October 16.
She was taken to an unidentified hospital three days later and is recovering in intensive care. Her condition is unknown.
A state and federal investigation has been launched to find out how exactly she contracted the plague.
There have been eight cases in Oregon in the last 20 years, with none of them leading to deaths.
In 2012, an Oregon man lost his fingers and toes to the plague. He had contracted the disease from his cat after trying to remove a mouse from his cat's throat.
There have been 16 cases of bubonic plague in the US this year including this one, with four people dying.
Four patients with plague were in Colorado, with four more in New Mexico, two in Arizona and Oregon and one in California, Utah, Georgia and Michigan.
The Michigan case last month was the first ever in the state.
The Michigan Department of Health and Human Services said the patient – who was also not named – was recovering.
The infectious bacterial disease is carried by squirrels, chipmunks and other wild rodents and their fleas. When an infected rodent becomes sick and dies, its fleas can carry the infection to other animals or humans through bites.
It is treatable with antibiotics if caught early, but can be fatal if left untreated. Bubonic plague is the most common form and is characterized by high fever, lethargy and swollen lymph nodes.
A plague vaccine is not available at this time.
Officials recommend people avoid any contact with wild rodents, especially sick or dead ones, and should never feed squirrels or chipmunks. People should also keep their pets away from wild rodents to avoid infection.
As much as I love squirrels, I'm not about to catch the plague simply to be able to say that I touched one.
Sierra Marlee
More articles by Sierra Marlee
These websites were ranked using Alexa. The number beside of each website represents its overall rank on the Internet. 1) Fox News: 158 2) Wall Street Journal: ...


text:
FROM golang AS build-env

COPY . /go/src/app
WORKDIR /go/src/app

RUN make build

FROM ubuntu
MAINTAINER Antoine GIRARD <antoine.girard@sapk.fr>

RUN apt-get update \
 && apt-get install -y glusterfs-client \
 && mkdir -p /var/lib/docker-volumes/gluster /etc/docker-volumes/gluster \
 && apt-get autoclean -y && apt-get clean -y \
 && rm -rf /var/lib/apt/lists/*

COPY --from=build-env /go/src/app/docker-volume-gluster /usr/bin/docker-volume-gluster

RUN /usr/bin/docker-volume-gluster version

ENTRYPOINT [ "/usr/bin/docker-volume-gluster" ]
CMD [ "daemon" ]

redpajama_set_name: RedPajamaGithub


text:
Nicole's Closing Resume
4360 Prospect Ave., Los Angeles, CA
1038 W 104th Los Angeles, CA
1320 N Edgemont St., Los Angeles, CA
3223 Pasadena Ave Los Angeles, CA
963 N Harvard Blvd., Los Angeles, CA
912 N Wilton Pl., Los Angeles, CA
4011 Country Club Drive, Los Angeles, CA
12206 Gault St., North Hollywood CA
2322 Sichel St., Los Angeles, CA
714 N St. Andrews Pl., Los Angeles, CA
6800 Variel, Canoga Park, CA
5721 Monte Vista St., Los Angeles, CA
1233 N Gordon St., Los Angeles, CA
4750 Eagle Rock Blvd., Los Angeles, CA
1601 N Kingsley Dr., Los Angeles, CA
10538 Cantara St., Sun Valley, CA
5459 Lubao Ave., Woodland Hills, CA
301 S. Irving Blvd., Los Angeles, CA
11759 Vanowen St., North Hollywood, CA
7600 Lindley, Reseda, CA
326 Robinson, Los Angeles, CA
2662 Pasadena Ave., Los Angeles, CA
1375 Sutherland St., Los Angeles, CA
4800 Lexington Ave., Los Angeles, CA
1529 Avalon St., Los Angeles, CA
4617 Eagle Rock Blvd. Los Angeles, CA
9124 S. Manhattan Pl., Los Angeles, CA
338 N Mariposa Ave., Los Angeles, CA
739 N Hudson Ave., Los Angeles, CA
1604 S Gramercy Pl., Los Angeles, CA
4350 Beverly Blvd., Los Angeles, CA
2006 Eastlake Ave., Los Angeles, CA
2110 Hancock St., Los Angeles, CA
1645 E. 6th St., Los Angeles, CA
2910 Acresite St., Los Angeles, CA
1120 S. Normandie Ave., Los Angeles, CA
2631 Granada St., .Los Angeles, CA
1201 N Mariposa Ave., Los Angeles, CA
306 N Bonnie Brae, Los Angeles, CA
519 N. Ardmore Ave., Los Angeles, CA
629 N Ardmore Ave., Los Angeles, CA
806 N Croft Ave., Los Angeles, CA
6911 Bellingham Ave., North Hollywood, CA
1114 S. Sherbourne Drive, Los Angeles, CA
6236 Willowcrest, North Hollywood, CA
1461 W. Sunset Blvd., Los Angeles, CA
1032 S. Bedford St., Los Angeles, CA
1811 Hi Point St., Los Angeles, CA
115 N. Catalina St., Los Angeles, CA
1220 N. Van Ness Ave., Los Angeles, CA
1543 N. Kenmore Ave., Los Angeles, CA
26866 Chaucer Place, Stevenson Ranch, CA
29142 Harmony Way, Saugus, CA
6459 Hazel Circle, Simi Valley, CA
1014 N El Centro Ave., Los Angeles, CA 90038
2707 W 14th St. Los Angeles, CA 90006
5742 La Mirada Ave. Los Angeles, CA 90038
311 S Witmer St. Los Angeles, CA 90017
2450 Naomi Ave Burbank, CA 91504
10618 Magnolia Ave. North Hollywood, CA 91605
15437 Vincennes St. North Hills, CA 91343
338 N. Mariposa Ave. Los Angeles, CA
149 S Vendome St. Los Angeles, CA 90057
127 N Catalina St. Los Angeles, CA 90004
559 N. St. Andrews Pl. Los Angeles, CA 90004
1408 N. Las Palmas Ave. Los Angeles, CA 90028
1120 S. Normandie Ave. Los Angeles, CA 90006
5720 Waring Ave. Los Angeles, CA 90038
1332 N. Sycamore Ave. Los Angeles, CA 90028
813 N Edinburgh Ave. Los Angeles, CA 90046
1236 N Edgemont St. Los Angeles, CA 90029
1406 Golden Gate Ave. Los Angeles, CA 90026
4264 Holly Knoll Dr, Los Angeles, CA 90027
700 Echo Park Ave. Los Angeles, CA 90026
Total Closing: $127,011,516


Discussion in 'Server Related' started by dtigercsk, Mar 8, 2019.
Because casual swearing is allowed but there is no need for the word gay whether it be calling people gay or discussing being gay or whatever. It is only ever really used for attacking people and discussing it has no place on a Minecraft server.


Overnight visits are a great way to explore what its like to be an Albright Student! We invite you to stay with a current student Sunday- Tuesday. We must receive your request 2 weeks prior to your stay date to prepare your visit.
If you are a recruited student-athlete, please work with the coach of your specific sport to schedule an overnight.
Please note that in order to participate in the overnight program, your Albright College Admission Application must be accepted. For more information about the application process, visit our website here.
Please note overnight visits are held Sunday through Tuesday. Dates are subject to change to accommodate for holidays, breaks, and finals week. We must receive your request two weeks prior to your intended stay.
If you would like to meet with an athletic coach, please indicate sport(s).
If you would like to meet with a professor, please indicate department(s).
* Offered as a co-major only. **Indicates a pre-professional program. ***Indicates a special program/minor.
Please make us aware of any additional requests.


text:
NewsRecent ArticlesReligion
German Churches Lost 430,000 Catholic and Protestant Members Last Year
The Catholic and Protestant churches both lost over 200,000 members last year. The losses have hit the churches hard, as members of both churches pay up to 9% of their taxable income as church taxes.
Germany's Catholic Church lost 216,078 members and Protestant churches lost some 220,000 in 2018, according to data published on Friday by the German Bishops' Conference and the Evangelical Church in Germany (EKD).
In total, around 23 million German citizens are still members of the Catholic Church and 21.14 million are members of the Protestant churches. The two groups account for 53.2% of the country's total population of over 83 million.
Hans Langendörfer, secretary of the German Bishops' Conference, described Friday's figures as a "worrying" statistic.
"Every departure hurts," said Heinrich Bedford-Strohm, president of the EKD. "Since people today, unlike in the past, decide out of freedom whether they want to belong to the church, it is important for us today to make even clearer why the Christian message is such a strong basis for life."
Falling for years
Church membership can officially be renounced by making a declaration in person at a local government agency, sometimes a district court. There is no need to provide any reason for wanting to leave.
Unless they renounce their membership with an official declaration, members of the Catholic and Protestant churches pay up to 9% of their taxable income as church tax, generating billions of euros in income for both organizations. The money is automatically deducted, just like payroll taxes or social insurance.
Membership set to halve by 2060
A study published by the University of Freiburg in May concluded that the number of people belonging to Germany's two main churches will drop by half by 2060.
The main reasons for declining membership in the two churches include adults leaving the church, fewer baptisms and an aging population, the researchers said.
The study predicted that the combined membership in the two churches will drop from about 45 million now to 34.8 million by 2035 and 22.7 million by 2060.
Although no statistics exist regarding the reasons for the departures, surveys conducted by officials in recent months suggest a connection to sexual abuse scandals involving the Catholic Church.
Reference: DW.com
Poisons in the Air, Water and Food, but Alcohol Rarely Ever Gets Mentioned Previous post
DNA, Apes & the Demiurge: What Interfered With Our Genetic Structure? Next post
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"Lying Pieces of Sh*t!" Man at Virginia Gun Rally Interrupts Live MSNBC Broadcast
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Company Is Using Plastic Bottles To Make Roads That Last 10x Longer Than Asphalt
NewsSatanist Pedophiles
45-Year-Old Man Caught With Child Pornography Says He Identifies as an 8-Year-Old Girl


Make plans to attend this fun Family Movie Night! Bring your kids and a blanket, we will empty out all the chairs in the auditorium and watch The Lego Batman Movie on the BIG SCREEN! This fun, free night is a great way to have some fun as a family this summer. Light refreshments will be available, so join us, July 28th at 7pm.


Phil Lo Greco, a Canadian professional boxer and a former WBC International Welterweight Champion arrived in Opulence Executive Travel's Rolls Royce Phantom for a press conference in Liverpool at the Hilton Hotel.
Amir Khan, a professional British boxer and boxing world champion, will be facing Phil Lo Greco in the boxing ring at the Echo Arena in Liverpool on 21 April 2018 live on Sky Sports. Former unified Light-Welterweight ruler Khan is back in action for the first time since May 2016 where he stepped up to Middleweight to face Mexican star Saul 'Canelo' Alvarez and now Khan is determined to get back on the road to World title glory in his first fight of a three-fight deal with Matchroom Boxing.


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Clinical & Medical Biochemistry
Editor Board
Upcoming Special Issues
Clinical & Medical Biochemistry received 152 citations as per Google Scholar report
Clinical & Medical Biochemistry peer review process verified at publons
Directory of Research Journal Indexing (DRJI)
Citations Report
Indexing and Archiving
Agri and Aquaculture
Bioinformatics & Systems Biology
Neuroscience & Psychology
Ramon Cacabelos, MD, PhD, DMSc
Professor & Chairman, Department of Biotechnology and Genomics
EuroEspes Biomedical Research Center, Continental University Medical School, Spain
GalilaAgam
Professor of Biochemistry, Department of Clinical Biochemistry and Pharmacology
Ben-Gurion University, Negev, Israel
Jianfei Yang
Department of Immunology, GlaxoSmithKline Pharmaceuticals Ltd
Abed N. Azab, Senior lecturer, PhD
Professor, Department of Nursing
Ben-Gurion University of the Negev, Israel
Krishnamurti Dakshinamurti, PhD
Emeritus Professor, Department Biochemistry and Molecular Biology
University of Manitoba, Canada
Christos Kroupis, MSc, PhD, EuSpLM
Assistant Professor, Department Clinical Biochemistry and Molecular Diagnostics
University of Athens Medical School, Greece
Davide Vigetti
Associate Professor, Department of Biochemistry
University of Insubria, Italy
Tomris Ozben, Ph.D., D.Sc. Med. Lab Speciali
Chairman, Department of Clinical Biochemistry
Akdeniz University, Turkey
SURESH M KUMAR, M.Sc., Ph.D.
Professor, Molecular Biology of Cancer
Penn State University, USA
Ammar Mohammed Hamood AL-Farga
University of Jiangnan, P.R. China
Riyaz Mohamed
Post-Doctoral Fellow, Department of Oral Biology
Augusta University, CB 2516, Augusta, Georgia
Farhan R. khan
Department of Medical Lab sciences
Shaqra University, Saudi Arabia
Srinivasan Krishnamoorthy
University of Pretoria, South Africa
Walsh Medical Media (WMM) presents peer-reviewed original research and analysis that deals exclusively with the treatment of schizophrenia and related psychoses and is directly relevant to patient care through
Office 317 Boundary House ,
Cricket Field Road, Uxbridge,
UB8 1QG, London UK


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SOFLOSOUND Previews – Tru Trilla Brings Back East Coast Aggression on God of Barz
Published on May 29, 2020 June 26, 2020 by trodriguez23547
New Jersey's Tru Trilla has dropped a hard hitting rap album in the form of God of Barz.
By: Tommy "T-Rod" Rodriguez
As a hip-hop fan, it feels good to run back an East Coast Classic every once in a while.
In a musical landscape as historic and diverse as rap music, there's room for both modern progression and nostalgic throwbacks in the weekly drop. Today's album, God of Barz by rapper/producer Tru Trilla, is a perfect example of the latter. The New Jersey native sounds hungrier than anyone I've heard in the past few months, making music that golden-age and modern fans can enjoy. Trilla's voice is ear-grabbing, his writing is nasty, and his aesthetics are gruff, making for a one of this year's finer underground releases.
Tru Trilla's all about kicking ass and taking names on God of Barz. His style is very confrontational throughout; while some rappers can come off as annoying while doing this, Trilla's forgoes that stereotype with raw delivery as he crosses names off his list. When he calls out other rappers' work as "dumpster juice" on the standout "Fake Views," I feel his energy surging through my headphones. Combine this confrontational approach to rapping with a consistently high-volume delivery, and the passion is palpable. As he raps "I paint pictures, y'all scribbling," I wince. Not in pain, but because his excellent delivery adds extra force to a sucker-punch of a line.
Speaking of punch, Trilla's writing has quite a bit of it. While he goes out of his way to hang other rappers' heads over his fireplace, he still manages to work in vivid tales of his upbringing, work ethic, and environment. Take the opener "God's Partical": Trilla's storytelling ability is on full display on the first verse of the song, painting a dangerous criminal tale with the detail of a movie. "Rebel" shows Trilla's passion for the roots of the genre, talking about the problems of today in a musical fashion and going against the grain. It's a sentiment rarely returned to in hip-hop today, so Trilla's words here hit hard amidst the rapid turntables. In addition to this, Trilla takes a break from the storytelling and discussion of American problems, opting to flex his rap skills, with "God's Sympathy" being a dive into fistfight rap. Teaming up with Fly Kwa and Confucius, the trio take turns comparing their rap skills to hand-to-hand combat. It works out surprisingly well, bringing lyrical firepower amidst snarling lion samples and blaring horns.
"Society tried to write us off and use propaganda to right their wrongs" Trilla raps on "Word of God." One of his greatest talents is to make particular lines jump out at you, and this lends an identity to several of the songs here; his hook game is well-groomed for what appears to be a throwback album. Even when songs emphasize the verses more, I find that his rapping matches the rhythm of the song in an engaging way, especially on the woodwinds of "Real Right." While it may seem to dive too much into the Wu-Tang battle rap cosmetic, God of Barz makes up for it with real content: lines to remember, snarling delivery, and plenty of attitude.
The production on God of Barz, thankfully, matches Trilla's attitude. The use of horn samples and string samples throughout add a great deal of tension to the mix, especially when Trilla gets more aggressive. Producer DNA Beatz adds a level of chunkiness to his drums that add a layer of oomph to the beat on "Word of God," while his use of sinister piano loops on "God of Barz" sounds like something MF DOOM would love. The overall vibe of the instruments is sinister, crunchy, and mean, a perfect backdrop to Trilla's voice.
Simply put, I think God of Barz is a great project. Tru Trilla really gave an excellent performance here, with his skillful writing and performance being the driving force behind some killer beats. If you're a fan of the hardcore East Coast style, good writing, or music with some force behind it, this is the one for you!
Must Listen Tracks: "God's Partical," "Word of God," "Fake Views," "God's Sympathy," "Real Right," "God of Barz," "The Rebel"
God of Barz had arrived to streaming services today, May 29th. Stream it below! Special thanks to Tru Trilla, New Dawn Records, and Urban Elite Promotions for an early preview!
Album Reviews, Previews Album Review, East Coast hip hop, God of Barz, God of Barz Album Review, Hardcore Hip Hop, New Dawn Records, Tru Trilla, Urbanelite Promotions
0 to 100/Catch Up Reviews- Kids See Ghosts
J. Cole- KOD Album Review
All Smiles- A Review of Anderson .Paak's Ventura
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Episode #63 - Psyche Terry Polished Podcast: Refining the Details of Life
Psyche Terry was born on the south side of Chicago to a teen mom. Due to a lack of resources, her parents gave custody to Psyche's great aunt. Based on the statistics of the community, Psyche was expected to be pregnant by 16, unwed, and working multiple low paying jobs to make ends meet. Her high school counselor told her that she would likely not go to college.
Psyche recognized patterns of bad habits and decisions early in life and was inspired and motivated to live her life differently. She earned a high school internship that led to a 12-year career at Whirlpool Corporation. It took Psyche only 3 years to graduate from Western Michigan University's Hayworth College of Business and despite failing the entrance exam 3 times, she later graduated 2nd in her class from UNLV's College of Business EMBA.
Psyche Terry is now blazing trails in the retail world as the Founder and CEO of UI Global Brands, a lifestyle brand that includes the Inspire PsycheTerry and Urban Hydration naturally inspired skincare, haircare, and lifestyle collections. She is the first African American female to successfully launch a line of lingerie that wholesales to the largest department, retail, off price, and beauty stores in the U.S. Psyche is an industry pioneer with an undeniable passion for women and entrepreneurship. A wife and mother of three children under the age of 8, Psyche leads marketing, design, and retail strategy teams for the thriving business, which she started with in 2009. Under her leadership UIGB experienced 700%+ sales growth in 2013 and in 2014 received a $40 million valuation. The company's products are available at more than 8,000 stores nationwide and select retailers in South America and the Caribbean.
Psyche's achievements as a businesswoman and motivational speaker have resulted in her being featured in top publications including Forbes, Wall Street Journal, Essence and Black Enterprise. She has also appeared on major television networks such as MSNBC, CNBC, ABC, CBS, and Univision. Psyche has appeared on NBC's top rated Steve Harvey as a mystery millionaire on one of the highest ranked episodes of the year. Psyche recently had the distinguished honor of being requested as a White House speaker on young women entrepreneurship and to meet Oprah.
Psyche tells stories of how she cashed out her Whirlpool 401(k), maxed out several credit cards, and flew her entire family to Asia in hopes of landing manufacturing and sales contracts. She wins hearts of moms and millionaires alike when they hear of her stories of being 36 weeks pregnant and opening a 40-person skin care factory.
Psyche and her college sweetheart husband, Vontoba, have offices next door to each other as they continue to lead and grow their company and brands in a true business love story.
Polished and Kat Armstrong


19 Jun ASTM E TESTING. FOR TPR2. ON. FLEXIBLE FIRESHELLIM. MASTIC AFES -M1. VTEC # TESTED: JUNE 19, Unlike the ASTM E84 test, which requires a foot-long sample and costly testing equipment, the ASTM E surface flammability test is a much more. ASTM E Standard Test Method for Surface Flammability ASTM E Fire Test to Building Material Standard ASTM E Standard Test Method for Surface.
The astm e162 is conducted using small specimens that are representative, to the extent possible, of the material or assembly being evaluated. Astm e162 test method may involve hazardous materials, operations, and equipment. The sample size of the E is astm e162 by e1662, while E84 requires a sample in by ft long, which is a rather large size to deal with. The values given in parentheses are mathematical conversions to SI units s162 are provided for information only and are not considered standard.
In addition, we proudly hold the following accreditations: Unlike the ASTM E84 test, which asfm a foot-long sample and costly testing equipment, the Astm e162 E surface flammability test is a much more affordable option that only requires six-inch by inch astm e162. ISO, Accredited Laboratory.
Astm e162 determined, the radiant panel index astm e162 helpful in assessing the overall fire hazard of the given material. You can trust the w162 of the results you receive since we have been a leader in testing services for more than 35 years. Referenced Documents purchase separately The documents listed below are referenced within the subject standard but are not provided as part of the standard.
For quick, affordable and reliable surface flame spread testing of coatings, building products, materials and textiles used in railroad, transportation, and military applications, VTEC Laboratories is your number one choice. It is the responsibility of the user of this standard to establish appropriate safety and astm e162 practices and determine the applicability astm e162 regulatory limitations prior to use.
Astm e162 test has not enjoyed the widespread adoption of ASTM E84, perhaps due to misconceptions concerning its ability to connect to E84 flammability performance measurements. Therefore, the results are valid only for the fire test exposure conditions described in this procedure. When Will I Receive Results? Above the material are a series of thermocouples mounted in a chimney, or ast, which measures the produced heat against known fuel sources.
Nevertheless, when viewed astm e162 its own frame of reference, this test method provides axtm valid flammability assessment.
Link to Active This link will always route to the current Active version of the standard. At Astm e162 Laboratories, a popular flammability test we regularly conduct on behalf of transportation and military interests astm e162 the American Society for Testing and Materials ASTM E for astm e162 flammability of materials using a radiant heat energy source.
It is intended for use in measurements of the surface flammability of materials exposed to fire. A heat evolution factor, Q, is determined from the max temperature rise during astm e162 testing 4.
E1662 E is currently applied to materials for rapid rail transit and light rail transit vehicles and ast, applications among others. This test asttm may involve astm e162 materials, operations, and equipment.
The tunnel specified by E 84 constitutes an expensive and cumbersome piece of apparatus. Once all samples and paperwork are received, we can complete the test for flame spread and have your results in just two weeks. Flame front propagation plot for 4. Therefore, the results are valid only for the fire test exposure conditions described in this procedure.
If the test results obtained by this test method are to be considered as part of an overall assessment of fire hazard in a building or structure, zstm astm e162 example criteria, concepts and procedures incorporated into Guide E shall be taken into consideration.
That awtm the fastest turnaround time in the flammability astm e162 industry. ASTM E provides a convenient relative measure of the flame spread and heat evolution performance of building materials.
It astm e162 the responsibility of the r162 of this standard to establish appropriate safety and health practices and determine astm e162 applicability of regulatory limitations e12 to use. There is no similar or equivalent ISO standard. Specific information about hazard is astm e162 in Section 7. It is not intended for use as a basis of ratings for building code purposes. Finally, these two factors are combined to find the radiant panel index Is as in the following equation:.
The test is conducted by placing the material at a degree angle to a radiant astm e162 source, which astm e162 a porous panel that steadily burns a mixture of natural gas and air at degrees Fahrenheit.
Specific information about hazard is given in Section. It should be noted, however, that 4 samples astm e162 required for E, whereas E84 requires only one. Its unique feature is that the sample is exposed to a 12 by in radiant heat source and reflects more accurately what might be found in adtm real fire environment.
Finally, these two astm e162 are combined to find the radiant panel index Astm e162 as in the following equation: First, the flame spread factor Fs represents the rate at which the test material spreads flames.