text:
\section{Introduction}

Deep learning has gained significant success in a wide range of applications, for example, automatic speech recognition (ASR)~\cite{dnn:dahl11:307,dnn:hinton12:141}. Typical deep
models used in ASR include deep neural networks (DNNs)~\cite{dahl2012context,deng2014} and recurrent neural networks (RNNs)~\cite{graves2014towards,sak2014long,weng2014recurrent,yu2015recurrent}. The success of the deep models is largely attributed
to various pre-training approaches that alleviate the under-fitting and over-fitting problem that had hindered
the development of complex neural models for a long time. Most of the well-known pre-training methods are layer-wise, which train simple
models and then stack them layer by layer to form the deep structure. This pre-training is mostly unsupervised, and is
usually followed by a fine-tuning step which refines the model in a supervised fashion. Two popular pre-training approaches are based on
restricted Boltzmann machines (RBMs)~\cite{hinton2006reducing,dahl2012context} and auto-associators~\cite{bengio2007greedy} respectively.

The basic idea of layer-wise pre-training is to divide the hard deep learning task into easier tasks
of training simpler shallow models. A theoretical analysis for its role in deep model training was presented by Bengio~\cite{bengio2007greedy},
and a through empirical analysis was provided by Erhan et al.~\cite{erhan2010does}.
These studies show that the layer-wise pre-training plays a role of regularization that locates the model
to a `good' place in the parameter space so that the succeeding supervised training (aka fine-tuning) is
easy to find a good local minimum. Recently, the effectiveness of
layer-wise pre-training is proved by Paul et al. using the group theory~\cite{pauldoes}. In ASR, Yu and
colleagues reported that a layer-wise discriminative pre-training can obtain similar performance as
the layer-wise unsupervised pre-training does~\cite{yu2010roles,seide2011feature}.
It is now widely accepted that the layer-wise pre-training makes its contribution in two ways:
(1) it can discover hierarchical patterns by which invariant high-level feature can be obtained;
(2) it can initialize deep models in a health state so that the supervised training can be conducted more effectively.

Although theoretically sound and empirically effective, the layer-wise pre-training is limited to multi-layer models.
For models without a clear layer-wise structure, they can not be easily pre-trained by the existing methods. As an example,
the RNN model does not involve a clear layer-wise structure and the model is complicated by the hidden-hidden connections.
To pre-train this model, either an ad-hoc treatment is required or a special pre-training model needs designing.
For example, Vinyals et al. \cite{vinyals2012revisiting} proposed a two-stage approach: in the first stage,
the hidden-hidden connections are cut off and only the forward paths are trained, and in the second stage, the
entire network is optimized. This approach is obviously suboptimal since the recurrent path is not pre-trained together with the
forward path.
Pasa and colleagues~\cite{pasa2014pre} constructed a linear autoencoder on sequential data to pre-train the RNN model.
This linear autoencoder model exactly matches the RNN structure so that all the parameters can be jointly pre-trained.
Following the same idea, Boulanger-Lewandowski et al.~\cite{boulanger2012modeling} proposed a recurrent temporal RBM
model to match the RNN structure. These task-specific pre-training models need to be specifically designed,
which is certainly not ideal. Moreover, if the target model is complex, e.g., with cross-layer
connections, it would be difficult to design an appropriate pre-training model, and training such a model by
unsupervised learning is often a prohibit task.

This paper presents a simple yet powerful pre-training approach based on knowledge transfer, which is largely motivated by the logit matching approach from Ba et al.~\cite{ba2014deep} and the dark knowledge distiller model from Hinton and colleagues~\cite{hinton2014distilling}. The basic idea is that a well-trained model involves rich knowledge and can be used to guide the training of other models. In Ba and Hinton's work~\cite{ba2014deep,hinton2014distilling}, this idea was applied to learn simple models from complex models or model ensembles~\cite{ba2014deep,hinton2014distilling}.
In ASR, Li et al. has applied the same idea to train small DNNs from a large and complex DNN~\cite{li2014learning}.
We show in this paper that knowledge transfer is a general approach and can be used in a very different way. Instead of learning simple models from
complex models, it can be used to pre-train complex models using a simpler model. Specifically, it is possible to train a simple model and then
use this model as a teacher to guide the training of a complex model (child model) that is normally difficult to accomplish. This teacher model might be rather weak, but it is sufficient to direct the child model where to go. Once the teacher model helps the child model reach a reasonable place in
the parameter space, the child model can learn by itself and finally finds a good local optimum, delivering a performance even better than
the teacher model.

This weak teacher strategy is rather different from the idea of logit matching and dark knowledge distillation proposed in~\cite{ba2014deep,hinton2014distilling}. The teacher model plays a role of `supervisor' instead of a `teacher', and the
teaching process is essentially a pre-training. The self-learning of the child model, correspondingly, is a fine-tuning.
In fact, the teaching processing (pre-training) is the same as the dark knowledge distillation: the teacher model is firstly trained
and then is used to generate targets for the training data. These targets are actually posterior probabilities and so are
`soft' compared to the original one-hot `hard' targets. The soft targets are used to train the child model. As we will see,
using soft targets leads to a smoother objective function, which makes the pre-training a much easier task than training with
the original hard targets.

Our experiments on an ASR task with the Aurora4 database demonstrated three interesting findings: (1) the knowledge transfer pre-training
can be used to train RNNs, which is challenging with conventional methods; (2) the pre-training can use a very weak teacher
model; (3) combining the knowledge transfer pre-training  and the conventional RBM  pre-training delivers additional gains.

The reset of the paper is organized as follows. Section~\ref{sec:rel} briefly discusses some related works, and~\ref{sec:method} presents the knowledge transfer pre-training. Section~\ref{sec:exp} presents the experiments, and the paper is concluded by Section~\ref{sec:con}.

\section{Related to prior work}
\label{sec:rel}

This study is directly motivated by the work of dark knowledge distillation from Hinton~\cite{hinton2014distilling}. The important distinction
is that we use simple models to teach complex models. The teacher model in our work in fact knows not so much, but it is sufficient to provide a rough guide that is important to train complex models, such as highly deep DNNs or multi-layer RNNs. More precisely, the existing methods use the teacher model as a knowledge source, while our method uses the teacher model for pre-training.

Our work is also related to the FitNets approach proposed by Romero et al.~\cite{romero2014fitnets}, where a teacher model is used to
supervise the learning of another network which can be in a different structure, e.g., deeper and fatter. Particularly, they learned
hidden layers instead of output layers, which is a big advantage in transferring hierarchical knowledge into child models.
Our approach focuses on learning the output layer, which does not
consider the internal structure of the teacher model, and so is truly `blind learning'. This offers more flexibility to pre-train complex and heterogeneous models, though looses the advantage of learning hierarchical patterns.

Another related work is the HMM-based pre-training approach recently proposed by Pasa and colleagues~\cite{pasahmm}.
In that work, the authors train an HMM model, and then use the trained model to generate training
data. The generated data are then used  to pre-train RNN models. This approach shares the same idea
of knowledge transfer pre-training as our work. The main difference is that the knowledge transfer in Pasa's approach is based on
some randomly sampled data, which essentially simulates the joint distribution of the data and their target labels;
whereas our approach is based on targets predicted by the teacher model, which simulates
the conditional distribution of the targets given the data.

\section{Pre-training with dark knowledge transfer}
\label{sec:method}

\subsection{Dark knowledge distiller}

The idea that a well-trained DNN model can be used as a teacher to help training other models was proposed by Ba and Hinton~\cite{ba2014deep,hinton2014distilling,li2014learning}. The basic assumption is that the teacher model
learns rich knowledge from the training data and this knowledge can be used to guide the training of child models
which are simple and hence unable to learn many details without the teacher's guide. To distill the knowledge from
the teacher model,
the logit matching approach proposed by Ba~\cite{ba2014deep} teaches the child model by encouraging
its logits (activations before softmax) close to those generated by the teacher model in terms of square error, and the dark
knowledge distiller model proposed by Hinton~\cite{hinton2014distilling} encourages the output
of the child model close to those of the teacher model in terms of cross entropy. This knowledge transfer
idea has been applied to learn simple models from complex models so that the simple model can
approach the performance of the complex model~\cite{li2014learning,chan2015transferring}.

We focus on the dark knowledge distiller model rather than logit matching as it showed better performance in our experiments.
This model uses a well-trained DNN as the teacher model to predict the targets of the
training samples, and these targets are used to train
the child model. The predicted targets are actually posterior probabilities
of the targets associated with the DNN output, and they are soft because the class identities
with these targets are not as deterministic as with the original one-hot hard targets.
To make the targets softer, a temperature $T$ was introduced in~\cite{hinton2014distilling} to
scale the logits. This is formulated by $p_i = \frac{e^{z_i/T}}{\sum_j e^{z_j/T} }$,
where $i,j$ indexes the target classes.
As argued by Hinton~\cite{hinton2014distilling}, a larger $T$ allows
more information of non-targets to be distilled.

\subsection{Knowledge transfer pre-training}

In the original proposal~\cite{hinton2014distilling}, knowledge transfer was used to
train simple models with a complex model, and the goal is to achieve a
light-weighted model that can approach to the performance of the complex
model. We argue in this paper that knowledge transfer is a general
method and can be used to pre-train complex models with a simple model.

The basic assumption is that soft targets lead to a smoother objective function,
and so training with them is easier than training with the original hard targets.
Intuitively, soft targets offer probabilistic class labels which are not
as deterministic as hard targets. This matches the real situation where
uncertainty always exists in classification tasks. For example, in speech
recognition, it is often difficult to identify the phone class of a frame
due to the effect of co-articulation. Moreover, the uncertainty associated
with soft targets blurs the decision boundary of correct and incorrect targets.
The smoothness associated with soft targets has been stated in~\cite{hinton2014distilling},
where it was argued that soft targets result in less variant
gradients between training samples. This is equal to say that
the objective function is smooth. A smooth objective function is certainly
much easier to optimize, and in the case where the targets are
extremely soft (i.e., $T$ goes to infinity), the objective function
becomes flat and the optimization is trivial.

The ease of training with soft targets can be used to simplify training
complex models. Generally speaking, complex models (e.g., very deep or
with recurrent connections) involve a large amount of parameters
or complex dependencies among variables, which leads to twisted
objective functions that are hard to optimize~\cite{bengio1994learning,sutskever2013importance}.
To solve the problem, conventional layer-wise pre-training breaks
a complex model to simpler models that can be easily trained
individually, and then stack them back to form the complex model. The smoothness
on objective functions offered by knowledge transfer learning in the form of soft targets
provides a different way to simplify complex model training: instead of
breaking the complex model into simple models, we replace the twisted
objective function with a smoother one by using soft targets when training
the model.
By this approach, the difficulty in complex model training is greatly reduced, and the optimization
can be conducted on the entire model instead of a single layer as in layer-wise
pre-training. As long as the smoothed objective function possesses
a similar trend as the original objective function in gradients, training with the smoothed function would
result in a good initialization for the model parameters.

Note that learning soft targets is not the ultimate goal of the model training,  so
a fine-tuning step is required to refine the model with the original hard targets. In this sense,
the knowledge transfer learning is a pre-training step, which initializes the model
parameters in such a way that the fine-tuning has a good starting point to
reach a better local minimum, compared to training with hard targets from the beginning.

The knowledge transfer pre-training is related to the curriculum training approach discussed
in~\cite{romero2014fitnets}, where training samples that are easy to learn are
firstly selected in model training, while more difficult samples are selected
later when the model is strong enough. In knowledge transfer pre-training,
the soft targets can be regarded as easy samples and so are firstly used (in pre-training),
and hard targets are difficult samples and are used later (in fine-tuning).

We highlight that for knowledge transfer pre-training, the teacher model
is not necessarily very strong. The goal of the pre-training is to provide a
good initialization for fine-tuning, instead of knowledge transfer from one model to another,
so a model with reasonable quality is sufficient to be a teacher, although more
intelligent teachers are generally welcome.

\subsection{Comparison with layer-wise pre-training}

A confirmed advantage of layer-wise pre-training is that it can discover hierarchical patterns of
the input signal by unsupervised learning. This hierarchical patterns discovering is desirable for
several reasons: it is consistent with the information processing strategy
in human brains, and it can find invariant high-level features that are robust against noise and corruption.
A potential problem of layer-wise pre-training, however, is that the patterns are learned in an unsupervised
fashion, which means that they are purely derived by statistics without considering the task in hand.
For example in speech recognition, less frequently occurred patterns such as rare consonant phones
are difficult to discover, however they are important for the recognition tasks.

The knowledge transfer pre-training, on the other hand, is purely supervised and so it is
high greedy towards the target task. Additionally, this approach pre-trains the
entire model and so tends to be fast. Finally, it can pre-train models without clear multi-layer structures.
The disadvantage is, it is just a functional mimic to the teacher model
without considering any internal structure of the teacher model. Therefore,
it can neither discover any hierarchical patterns, nor learn them from the teacher model.

An interesting idea is to combine different types of pre-training methods.
For example, we can use layer-wise pre-training to discover hierarchical patterns, and then use knowledge transfer
pre-training to promote the patterns that are most important to the task. A simple approach
investigated in this paper is to employ the RMB pre-training and the knowledge transfer pre-training
sequentially, so that the advantages of both methods are leveraged.

\section{Experiments}
\label{sec:exp}

The proposed knowledge transfer pre-training is applied to train acoustic models for ASR systems. In the first experiment,
the knowledge transfer pre-training is used to train RNNs with a DNN as the teacher model. In the second experiment,
the knowledge transfer pre-training is compared with RBM pre-training and layer-by-layer supervised pre-training, and the combination
of knowledge transfer pre-training and RBM pre-training is also investigated.

\subsection{Data and baseline}

The experiments are conducted on the Aurora4 database in noisy conditions, and the data profile is largely standard: $7137$ utterances for model training,  $4620$ utterances for development and $4620$ utterances for testing. The Kaldi toolkit\footnote{http://kaldi.sourceforge.net/} is used to conduct the model training and performance evaluation, and the process largely follows the Aurora4 s5 recipe for GPU-based DNN training. Specifically, the training starts from constructing a system based on Gaussian mixture models (GMMs) with the standard $13$-dimensional MFCC features plus the first- and second-order derivatives. A DNN system is then trained with the alignment provided by the GMM system. The feature used for the DNN system is the $40$-dimensional Fbanks. A symmetric $11$-frame window is applied to concatenate neighboring frames, and an LDA transform is used to reduce the feature dimension to $200$, which forms the DNN input. The DNN architecture involves $4$ hidden layers and each layer consists of $2048$ units. The output layer is composed of $2008$ units, equal to the total number of Gaussian mixtures in the GMM system. The cross entropy is used as the training criterion, and the stochastic gradient descendent (SGD) algorithm is employed to perform the training.

\subsection{Knowledge transfer pre-training for RNN}

To train the RNN acoustic models, the DNN model of the baseline system is used as the teacher model.
The RNN is based on the LSTM structure, where the input features are the $40$-dimensional Fbanks, and the output units correspond
to the Gaussian mixtures as in the DNN model. The momentum is empirically set to $0.9$, and the starting learning rate is set to $0.0001$ by default.

The experimental results are reported in Table~\ref{tab:res}. The performance is evaluated in terms of two criteria: the frame accuracy (FA) and the word error rate (WER). While FA is more related to the training criterion (cross entropy), WER is more important for speech recognition. In Table~\ref{tab:res}, the FAs are reported on both the training set (TR FA) and the cross validation set (CV FA), and the WER is reported on the test set.

In Table~\ref{tab:res}, `RNN [raw]' is the RNN baseline trained with hard targets directly. `RNN [prt.]' denotes systems after knowledge transfer pre-training,
and `RNN [prt.+ft.]' denotes systems with both knowledge transfer pre-training and fine-tuning. Two settings of the temperature ($T$) are evaluated ($T$=1 and $T$=2),
and the performance with one and two LSTM layers are reported respectively.

\begin{table}[!htb]
\caption{Results with RNN Models}
\label{tab:res}
\centering
\begin{tabular}{l|c|c|c|c|c}
\hline
               & \# LSTM  &  T   & TR FA\% &CV FA\%   & WER\% \\
\hline
DNN [4 hidden layers] & 0   &  -   & 63.1 & 45.2   & 11.40 \\

\hline
RNN [raw]            & 1  & -    & 67.3 & 51.9   & 13.57 \\
RNN [prt.]           & 1  & 1    & 59.4 & 49.9   & 11.46 \\
RNN [prt.+ft.]       & 1  & 1    & 65.5 & 54.2   & 10.71 \\
RNN [prt.]           & 1  & 2    & 58.2 & 49.5   & 11.32 \\
RNN [prt.+ft.]       & 1  & 2    & 64.6 & 54.1   & 10.57 \\
\hline
RNN [raw]            & 2  & -    & 68.8 & 53.2   & 12.34 \\
RNN [prt.]           & 2  & 1    & 60.4 & 50.6   & 11.11 \\
RNN [prt.+ft.]       & 2  & 1    & 66.6 & 55.4   & 10.13 \\
RNN [prt.]           & 2  & 2    & 58.6 & 49.7   & 11.26 \\
RNN [prt.+ft.]       & 2  & 2    & 65.8 & 55.2   & {\bf 10.10} \\
\hline
\end{tabular}
\end{table}

From the results, it can be observed that the RNN baseline (RNN [raw]) can not beat the DNN baseline in terms of WER, although much effort has been devoted to calibrate the training process, including various trials on different learning rates and momentum values. This is consistent with the results published with the Kaldi recipe. Note that this does not mean RNNs are inferior to DNNs. From the FA results, it is clear that the RNN models are better in terms of frame accuracy. Unfortunately, this advantage is not propagated to the WER results on the test set. Additionally, the results shown here can not be interpreted as that RNNs are not suitable for ASR (in terms of WER). In fact several researchers have reported better WERs with RNNs than with DNNs, e.g.,~\cite{graves2014towards,sak2014long,weng2014recurrent}. Our results just say that with the Aurora4 database, the RNNs with the \emph{basic} training method do not generalize well in terms of WER.

This problem can be largely solved by the knowledge transfer pre-training. It can be seen from Table~\ref{tab:res} that with the pre-training only, the RNN systems obtain equal or even better performance in comparison with the DNN baseline, which means that the knowledge learned by DNN helps the RNN models move out of bad local minima that are caused by the complex objective function. Paying attention to the FA results, it can be seen that the pre-training does not improve FAs on the training set, but better FAs on the CV set and better WERs on the test set are obtained. This indicates that the pre-training leads to models that are more generalizable with respect to both datasets and evaluation metrics. After the fine-tuning with hard targets, the performances of RNN systems are significantly improved. Additionally, it can be found that a larger $T$ leads to worse FAs on both the training and CV datasets, but better WERs on the test dataset. This indicates that knowledge transfer pre-training contributes by delivering a more generalizable model instead of a more optimized model.

When comparing the RNNs that involve one and two LSTM layers, it can be found that the two layers of LSTMs deliver better performance.
Note that two layers of
LSTMs are rather complex in structure, and so pre-training it with layer-wise unsupervised models (e.g., RMBs) is rather difficult.  With knowledge transfer, the pre-training is rather simple.

\begin{table}[!htb]
\caption{RNN Results with a Weak DNN for Pre-Training}
\label{tab:res-single}
\centering
\begin{tabular}{l|c|c|c|c}
\hline
                    & T              & TR FA\% &CV FA\%   & WER\% \\
\hline
DNN [1 hidden layer]      & -              & 61.2 & 42.5   & 13.10 \\
RNN [raw]           & -              & 68.8 & 53.2   & 12.34  \\
\hline
RNN [prt.]          & 1              & 57.2 & 48.6   & 12.69 \\
RNN [prt.+ft.]      & 1              & 65.3 & 55.0   & 10.72 \\
RNN [prt.]          & 2              & 54.4  & 46.8  & 13.20   \\
RNN [prt.+ft.]      & 2              & 64.7  & 54.7  & {\bf 10.60}	 \\
\hline
\end{tabular}
\end{table}

Another interesting investigation is to use a very weak teacher model to conduct the pre-training. A DNN with only 1 hidden layer of 2048 units (not deep actually)
is trained and used as the teacher model. This model is much weaker than the DNN baseline which involves 4 hidden layers. The results presented in Table~\ref{tab:res-single} show that even with the weak model, the
pre-training works fairly well, although not as well as with the original strong teacher model. This results confirm our conjecture that
the teacher model is not necessarily very strong.  The principle role of the teacher is not to teach all the details
to the student, but a correct direction with which the student can learn by itself.

\subsection{Comparison of pre-training methods}

Compared to RNN, the DNN model is much simpler. After the extensive research in recent years, training DNNs is not a problem any more. For example in
speech recognition, training a DNN model with more than $5$ layers is rather simple even without any pre-training techniques~\cite{seide2011feature}.
In this experiment, we apply various pre-training methods to train DNN models. The goal is not to demonstrate the necessity of pre-training
in DNN model training, but to compare different pre-training approaches.

For a better comparison, we use a new DNN baseline which involves $4$ hidden layers and each layer involves $1024$ units (it was $2048$ in the RNN experiment);
further more, no LDA was employed for the input feature. This setting makes the training a little difficult as less hidden units need to find
the most discriminative input from larger feature vectors. With the original DNN baseline, the pre-training methods didn't show much help,
particularly with the layer-wise methods.

\begin{table}[!htb]
\caption{DNN Results with Various Pre-Training Methods}
\label{tab:res-com}
\centering
\begin{tabular}{l|c|c|c}
\hline
                                 & TR FA\% &CV FA\%   & WER\% \\
\hline
DNN-4H [4 hidden layers]         & 57.3 & 44.1   & 12.22  \\
DNN-1H [1 hidden layer]          & 54.8 & 41.7   & 13.90  \\
\hline
RBM                              & 58.1 & 45.8   & 11.42  \\
Layer-by-layer Discriminative    & 61.1 & 43.8   & 12.16  \\
Knowledge Transfer (DNN-4H)      & 60.0 & 45.6   & 11.43  \\
Knowledge Transfer (DNN-1H)      & 59.6 & 45.1   & 11.65  \\
\hline
RBM + Knowledge Transfer (DNN-4H)  & 59.5 & 46.2   & {\bf 11.13}  \\
RBM + Knowledge Transfer (DNN-1H)  & 59.4 & 46.1   & 11.25  \\
\hline
\end{tabular}
\end{table}

We compare three pre-training methods: the RBM-based pre-training~\cite{hinton2006reducing}, the layer-by-layer discriminative
pre-training~\cite{yu2010roles}, and the proposed knowledge transfer pre-training. For the knowledge transfer pre-training, two
teacher models are tested: one is the DNN baseline that involves 4 hidden layers (DNN-4H),
and the other is a simpler model with only 1 hidden layer (DNN-1H). Note that for
the knowledge transfer pre-training, the classification layer (the affine transform before softmax)
needs to be re-initialized randomly after pre-training, otherwise it would be difficult for
the fine-tuning to achieve reasonable improvement.

The results are reported in Table~\ref{tab:res-com}. It can be observed that the RBM pre-training and the knowledge transfer pre-training
(DNN-4H as the teacher model) achieve significant performance improvement, and their performance are rather similar ($11.42$ vs. $11.43$).
With the weak one-layer DNN as the teacher model, the result is slightly worse than with the four-layer DNN, but it is still
rather good. The layer-by-layer discriminative pre-training does not show much contribution in this experiment. These results demonstrate
that the knowledge transfer pre-training works at least as well as the state-of-the-art layer-wise pre-training methods.

Finally, the RBM approach and the knowledge transfer approach are combined, where the RMB approach conducts layer-by-layer unsupervised
pre-training and after that the knowledge transfer approach conducts supervised pre-training on the entire network. Fine tuning is finally conducted
to achieve the best model. The results are shown in Table~\ref{tab:res-com} as well. It can be seen that this combination leads to the
best performance ($11.13$) that we can obtain on this task. This demonstrates that the two pre-training methods are complementary, and
the combination can leverage their respective advantage.

\section{Conclusion}
\label{sec:con}

We proposed a novel pre-training approach based on knowledge transfer learning. Compared to conventional layer-wise
pre-training methods that initialize a complex network by stacking simple models layer by layer, the knowledge transfer pre-training conducts the
initialization by a smooth objective function. As a supervised pre-training it is more task-oriented, and as an entire-network
pre-training it is faster. The experimental results on the ASR task demonstrated that the new pre-training approach can
effectively help training complex models, even with a weak teacher model. For example, a DNN model has been successfully
used to pre-train an RNN model. Compared to RMB pre-training
and layer-by-layer discriminative pre-training, the new approach leads to comparable or even better performance. Additionally,
the RBM pre-training and the knowledge transfer pre-training can be combined, which has lead to additional performance gains
in our experiments. The future work involves studying knowledge transfer between heterogeneous models, e.g., from
probabilistic models to neural models.

\bibliographystyle{IEEEtran}
\small{

redpajama_set_name: RedPajamaArXiv


Members of the Flower Gals attended a workshop on hardy perennial fuchsias in the Milton Fuchsia Demonstration Garden conducted by the members of the Puyallup Valley Fuchsia Society who started the garden and care for it .
We learned how to plant and grow fuchsias in the ground, when to prune them, and how to propagate them. We also learned about other fuchsia demonstration gardens in the area and the Northwest Fuchsia Society. Members then propagated fuchsias from cuttings and took the starts home to nurture.


James Grundy, an 87-year-old British veteran of the Korean War, will share his experiences of serving in a recovery unit during the war at Nam District Office in Busan on Wednesday.
"My talk will mainly be about the losses of life during the Korean War, but I will also be talking about the civilian loss," Grundy told the Korea JoongAng Daily in an email.
He has been coming back to Korea every year for the past 30 years to visit the Unmck since he was invited by the Korean Ministry of Patriots and Veterans Affairs in 1988. "I return every year to visit my fallen comrades and to wonder at the beautiful surroundings they rest in," he said.
The cemetery holds a total of 2,297 soldiers, including 884 from Britain, 462 from Turkey, 378 from Canada, 281 from Australia and 117 from the Netherlands. The U.S. soldiers were all sent back home as per an agreement at the time, but 36 asked to be buried at the Unmck in their wills and are buried at the cemetery.
Grundy's lecture, set to begin at 3 p.m. Wednesday at the Nam District Office auditorium, is open to anyone and is free. "We are hoping to let many foreigners as well as locals in Busan know about the lecture," said Kim Sung-han, an official of the Nam District Office.
The district office will present Grundy with honorary citizenship from Nam District during his visit for his continued assistance in taking care of the graves of fallen soldiers at the Unmck "Mr. Grundy is the second foreigner ever to receive this citizenship of Nam District, after we presented it to Anton Trauner, a German priest who dedicated 50 years of his life to the livelihood and welfare of the residents of Nam District," the district office said in a statement.
Grundy is currently fighting cancer and said that he hopes to be buried at the cemetery in the future as well.
"The management body of the Unmck, which is made up of members from 11 countries, has approved this request," said Park Eun-jung, an official of the Unmck.


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LOS ANGELES – Katten Muchin Rosenman LLP is pleased to announce the addition of Carol Johnston as a partner in its Private Wealth Practice. Ms. Johnston will be based in the Firm's Los Angeles office.
"Carol is one of the most respected estate planning attorneys in Southern California and we are delighted to add her considerable legal talents to our Los Angeles office," said Gail Migdal Title, managing partner of Katten Muchin Rosenman's Los Angeles office. "The addition of Carol reflects the Firm's continuing commitment to providing comprehensive Trusts and Estates services to clients in the Firm's key market segments."
Prior to joining the Firm, Ms. Johnston spent twenty years as an attorney in the Tax and Estate Planning Practice at O'Melveny & Myers LLP in Los Angeles. There, she specialized in estate and tax planning for individual clients, the representation of fiduciaries, and the administration of estates and trusts.
Ms. Johnston's experience includes planning and drafting revocable and irrevocable trusts, insurance planning, premarital planning, charitable gift planning, and all aspects of estate administration and probate procedures. She has been certified as a specialist in Estate Planning, Trust, and Probate Law by the State Bar of California, Board of Legal Specialization. Ms. Johnston is also a member of the Los Angeles County Bar Association's Probate and Trust Law Section. She was named to The Best Lawyers in America list for 2005-2006 in Trusts and Estates, as well as a "Southern California Super Lawyer for 2005" in a survey conducted by Law & Politics Media Inc. that was published in Los Angeles magazine and the Southern California edition of Super Lawyers.
Ms. Johnston received her Bachelor of Arts, magna cum laude, from Middlebury College in 1976 and her juris doctor from the University of Chicago Law School in 1979.
Carol A. Johnston
Los Angeles – Century City
Contact by email V-card
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redpajama_set_name: RedPajamaCommonCrawl


Phd Essay: Wordpress thesis custom header delegate assignments to professionals!
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text:
Bringing back bushfoods: Australia's landscape mapped to boost 'bush tucker' plants
by James Cook University
Credit: James Cook University
Growing native bushfoods could reverse environmental degradation and offer better food security. But how do we get bushfoods in the agricultural sector in a market saturated by modern crops?
New research from James Cook University's TropWATER has mapped Australia's entire landscape to uncover the best places to grow more than 170 bushfoods.
The study found the Great Barrier Reef catchment area to be a hotspot for a wide range of bushfoods including those most in-demand commercially, including lemon myrtle, native plums and bush tomatoes.
Author Dr. Adam Canning said identifying what native crops can grow where was an important first step in scoping potential native food industries to support farmers.
"Native foods in Australia have a rich history and there is a growing demand to get bushfoods in the supermarket, yet the commercial production of native foods remains small," he said.
"This research maps Australia's entire landscape to identify exactly what bushfoods can be grown where—and that's a big step toward boosting Australia's native food industry."
Dr. Canning said transitioning the agricultural landscape to include a diversity of native bushfoods would help reverse environmental degradation.
"Modern non-native crops such as sugarcane and wheat need intensive cultivation, irrigation, herbicides, and pesticides, and are grown as monocultures," he said.
"This comes at a cost to the environment, and we've seen this happen along the Great Barrier Reef catchment.
"Diversifying modern agricultural systems to include native plants would help restore balance in coastal ecosystems through reducing runoff, improving soil health and supporting biodiversity."
Coastal areas of Queensland's wet tropics, south-east Queensland, New South Wales, and Victoria were predicted to support the greatest diversity of native food and forage species.
"These areas are the most agriculturally intensive areas with degraded environments, but they also have the greatest potential for regenerative agricultural practices," he said.
"Farmers could start small by trialing intercropping, and slowly expand as knowledge and industries grow."
To further incentivise these practices, more financial benefit schemes need to be developed to reward farmers for providing ecosystem services, such as carbon sequestration and reduced pollution.
The research also indicates significant opportunity for Indigenous-led business models within the emerging bush foods sector. However, steps would need to be taken to ensure Indigenous knowledge and intellectual property are protected.
The research paper, "Rediscovering wild food to diversify production across Australia's agricultural landscapes," was published in Frontiers in Sustainable Food Systems.
More information: Adam D. Canning, Rediscovering wild food to diversify production across Australia's agricultural landscapes, Frontiers in Sustainable Food Systems (2022). DOI: 10.3389/fsufs.2022.865580
Provided by James Cook University
Citation: Bringing back bushfoods: Australia's landscape mapped to boost 'bush tucker' plants (2022, November 4) retrieved 30 January 2023 from https://phys.org/news/2022-11-bushfoods-australia-landscape-boost-bush.html
Agricultural rewilding can help restore the environment and support production of high-welfare food, researchers say
Research unlocks secrets of native rodents' rat race to new lands
America has corn and Asia has rice. It's time Australia had a native staple food
Researchers calculate the cost of restoring Australia's degraded ecosystems
Intensive farming is eating up the Australian continent, but there's another way
Enhancing revegetation of old fields in Western Australia
Honey bee colony loss in the US linked to mites, extreme weather, pesticides
Mixing between species may reduce vulnerability to climate change
New freshwater mussels discovered in southwestern Australia
Genetic diversity of the Saimaa ringed seal decreased in the 20th century as a result of human actions: Study


Now adolescents all over Australia can get involved in the Martin-Lysicrates Prize!
Six hundred high school students attended the second annual Martin-Lysicrates Prize at Riverside Theatres in September. And what a success it was!
The audience members made their democratic choice about which of the three plays should win the Prize—$15,000 to complete the play, and two return tickets to Athens.
But now the field is wide open again and students anywhere in Australia can vote for their choice. The live performances were filmed and can now be watched on the brand new Martin-Lysicrates website.
Anyone aged between 10 and 17 can vote for the play they would most like to see completed.
Not only is there a $3,000 prize for the playwright who secures the most viewers' votes, but there are lots of other prizes to be won by students and teachers in participating schools.
Teachers will find a whole unit of work to help prepare their classes to get the most out of the Martin-Lysicrates experience, and there are so many ways for kids to engage and come out winners themselves!
Along the way they will learn all sorts of things about ancient Greece, colonial Australia, democracy and the magic of theatre.
The Martin-Lysicrates experience is guaranteed to inspire.
Voting closes on December 7 and every vote counts!
Teachers and students—head to the Martin-Lysicrates website and get involved now!


text:
COVID-19: Osun Records 19 New Cases, Discharges 10 Patients
The Osun State Government has recorded another 19 new cases of coronavirus and discharged 10 Coronavirus patients from the State's Treatment and Isolation Centre.
This was disclosed on Monday in a statement by the Health Commissioner, Dr Rafiu Ismontu.
He said that the fresh cases are contacts of previously confirmed cases.
"A total of 19 new COVID-19 cases have been recorded in the State of Osun while 10 patients were on Monday discharged from the State's Treatment and Isolation Centre, following their recovery.
He also admonished residents of the State not to ignore the observance of all precautionary measures laid down by the State government.
The Commissioner revealed that with the latest detected cases, number of cases in the State had risen to 358 out of which 184 are active cases.
According to him, the State has successfully treated and discharged 166 patients, while eight deaths have so far been recorded.
COVID-19: Osun Records 19 New Cases
Discharges 10 Patients
Drama, Anxiety As Front-line Health Workers Undergo COVID-19 Test


Hi all! Today is a gray and rainy day, but yesterday the sun was shining so I managed to take some decent pictures!
I guess I've been busy since my last update (the pictures tell me so) but I don't feel like I've had much stitching time. We've just been so busy! My husband is away for work this week, so last night I made dinner, did my nails, watched Mr Selfridge & stitched. Not all at the same time clearly, but I did get some more progress on Plum Pudding then you'll see below. Not a ton, but I finished another motif.
It's cute, but I get bored working on it. I did see someone on Facebook omit some of the busy-ness and I liked that a whole lot more, so I might do the same thing.
It's a Dimensions Gold kit, so there are tons of blended threads and fractional stitches. It's going to look amazing when it's done.
I also started a freebie by Durene Jones (you can find it here). I was in a spring mood, I was hoping to finish by Easter but that didn't happen.
It's super cute and Durene has so many cute free patterns. She releases new ones every Friday on her facebook. I'm stitching it over 2 on 28 ct fabric that I had in my stash. It's a pale blue.
My goal for this past weekend was to finish the dark purple petals in the center, which I did manage to do! It's on 28 ct antique white fabric with the called for dinky dye threads.
Now I'm going around in a circle and trying it that way. I think I should start in the middle of a pattern instead of the top left, it's more gratifying to me to see it take shape that way.
I ordered Feburary's Fabric of the Month from Crafty Kitten for my Cherries Jubilee pattern and it came this past week!!
I didn't do a floss toss (I forgot) but one of my favorite things about Crafty Kitten is that she'll do custom sizes. This is a 20 x 20" cashel linen. I think it'll look awesome.
I also received a RAK from a friend in CO (no blog though).
I love cats AND she noted that I could probably finish it in one evening which is perfect for my attention span! Oh how she knows me well. It even came with fabric and the trim!
How's everyone's spring starting? Getting lots of stitching or is it slowing down with the warmer weather?
Nice progress on all of your WIPs! I always enjoy it when my husband travels for a few days--lots more time to stitch :) That little cat kit is adorable--I look forward to seeing your finish of it next time (the pressure's on!).
Beautiful stitching and lots of purple!
Great stitching! Nice progress on everything, especially Plum Pudding, and that little bunny start just looks sooo cute!
Your new fabric looks awesome as well :) Personally, I had two busy weeks but I hope to get back into my usual lazyness and stitching rhythm this weekend.
Lovely progress, plum pudding is gorgeous and pumpkin passport is so cute!
Your bunny designs looks cute.


text:
class GURL;

namespace base {
class FilePath;
class TaskRunner;
class Time;
}

namespace net {
class DrainableIOBuffer;
class IOBuffer;
}

namespace storage {
class BlobDataItem;
class BlobDataHandle;
class BlobDataSnapshot;
class FileStreamReader;

// The blob reader is used to read a blob.  This can only be used in the browser
// process, and we need to be on the IO thread.
//  * There can only be one read happening at a time per reader.
//  * If a status of Status::NET_ERROR is returned, that means there was an
//    error and the net_error() variable contains the error code.
// Use a BlobDataHandle to create an instance.
//
// For more information on how to read Blobs in your specific situation, see:
// https://chromium.googlesource.com/chromium/src/+/HEAD/storage/browser/blob/README.md#how-to-use-blobs-browser_side-accessing-reading
class COMPONENT_EXPORT(STORAGE_BROWSER) BlobReader {
 public:
  class COMPONENT_EXPORT(STORAGE_BROWSER) FileStreamReaderProvider {
   public:
    virtual ~FileStreamReaderProvider();

    virtual std::unique_ptr<FileStreamReader> CreateForLocalFile(
        base::TaskRunner* task_runner,
        const base::FilePath& file_path,
        int64_t initial_offset,
        const base::Time& expected_modification_time) = 0;

    virtual std::unique_ptr<FileStreamReader> CreateFileStreamReader(
        const GURL& filesystem_url,
        int64_t offset,
        int64_t max_bytes_to_read,
        const base::Time& expected_modification_time) = 0;
  };
  enum class Status { NET_ERROR, IO_PENDING, DONE };
  using StatusCallback = base::OnceCallback<void(Status)>;
  virtual ~BlobReader();

  // This calculates the total size of the blob, and initializes the reading
  // cursor.
  //  * This should only be called once per reader.
  //  * Status::Done means that the total_size() value is populated and you can
  //    continue to SetReadRange or Read.
  //  * The 'done' callback is only called if Status::IO_PENDING is returned.
  //    The callback value contains the error code or net::OK. Please use the
  //    total_size() value to query the blob size, as it's uint64_t.
  Status CalculateSize(net::CompletionOnceCallback done);

  // Returns true when the blob has side data. CalculateSize must be called
  // beforehand. Currently side data is supported only for single readable
  // DataHandle entry blob. So it returns false when the blob has more than
  // single data item. This side data is used to pass the V8 code cache which is
  // stored as a side stream in the CacheStorage to the renderer.
  // (crbug.com/581613).  This will still return true even after TakeSideData
  // has been called.
  bool has_side_data() const;

  // Reads the side data of the blob. CalculateSize must be called beforehand.
  // * Always calls the StatusCallback when the side data has been read.
  // * This may be done synchronously or asynchronously.
  // * The done callback will be called with Status::DONE or Status::NET_ERROR.
  // * If the callback returns NET_ERROR, net_error() will have the value.
  // Currently side data is supported only for single readable DataHandle entry
  // blob.
  void ReadSideData(StatusCallback done);

  // Passes the side data (if any) from ReadSideData() to the caller.
  base::Optional<mojo_base::BigBuffer> TakeSideData();

  // Used to set the read position.
  // * This should be called after CalculateSize and before Read.
  // * Range can only be set once.
  Status SetReadRange(uint64_t position, uint64_t length);

  // Reads a portion of the data.
  // * CalculateSize (and optionally SetReadRange) must be called beforehand.
  // * bytes_read is populated only if Status::DONE is returned. Otherwise the
  //   bytes read (or error code) is populated in the 'done' callback.
  // * The done callback is only called if Status::IO_PENDING is returned.
  // * This method can be called multiple times. A bytes_read value (either from
  //   the callback for Status::IO_PENDING or the bytes_read value for
  //   Status::DONE) of 0 means we're finished reading.
  Status Read(net::IOBuffer* buffer,
              size_t dest_size,
              int* bytes_read,
              net::CompletionOnceCallback done);

  // Returns if this reader contains a single MojoDataItem.  If so,
  // ReadSingleMojoDataItem can be called instead of multiple Reads as an
  // optimized path.  This can only be called after CalculateSize.
  bool IsSingleMojoDataItem() const;
  void ReadSingleMojoDataItem(mojo::ScopedDataPipeProducerHandle producer,
                              net::CompletionOnceCallback done);

  // Kills reading and invalidates all callbacks. The reader cannot be used
  // after this call.
  void Kill();

  // Returns if all of the blob's items are in memory. Should only be called
  // after CalculateSize.
  bool IsInMemory() const;

  // Returns the remaining bytes to be read in the blob. This is populated
  // after CalculateSize, and is modified by SetReadRange.
  uint64_t remaining_bytes() const {
    DCHECK_CALLED_ON_VALID_SEQUENCE(sequence_checker_);
    return remaining_bytes_;
  }

  // Returns the net error code if there was an error. Defaults to net::OK.
  int net_error() const {
    DCHECK_CALLED_ON_VALID_SEQUENCE(sequence_checker_);
    return net_error_;
  }

  // Returns the total size of the blob. This is populated after CalculateSize
  // is called.
  uint64_t total_size() const {
    DCHECK_CALLED_ON_VALID_SEQUENCE(sequence_checker_);
    DCHECK(total_size_calculated_);
    return total_size_;
  }

 protected:
  friend class BlobDataHandle;
  friend class BlobReaderTest;
  FRIEND_TEST_ALL_PREFIXES(BlobReaderTest, HandleBeforeAsyncCancel);
  FRIEND_TEST_ALL_PREFIXES(BlobReaderTest, ReadFromIncompleteBlob);

  BlobReader(const BlobDataHandle* blob_handle);

  bool total_size_calculated() const {
    DCHECK_CALLED_ON_VALID_SEQUENCE(sequence_checker_);
    return total_size_calculated_;
  }

  void SetFileStreamProviderForTesting(
      std::unique_ptr<FileStreamReaderProvider> file_stream_provider) {
    DCHECK_CALLED_ON_VALID_SEQUENCE(sequence_checker_);
    file_stream_provider_for_testing_ = std::move(file_stream_provider);
  }

 private:
  Status ReportError(int net_error);
  void InvalidateCallbacksAndDone(int net_error,
                                  net::CompletionOnceCallback done);

  void AsyncCalculateSize(net::CompletionOnceCallback done, BlobStatus status);
  // If this returns Status::IO_PENDING, it will take ownership of the callback
  // pointed at by |done| and call it when complete. |done| will not be
  // modified, otherwise.
  Status CalculateSizeImpl(net::CompletionOnceCallback* done);
  bool AddItemLength(size_t index, uint64_t length);
  bool ResolveFileItemLength(const BlobDataItem& item,
                             int64_t total_length,
                             uint64_t* output_length);
  void DidGetFileItemLength(size_t index, int64_t result);
  void DidCountSize();

  // For reading the blob.
  // Returns if we're done, PENDING_IO if we're waiting on async.
  Status ReadLoop(int* bytes_read);
  // Called from asynchronously called methods to continue the read loop.
  void ContinueAsyncReadLoop();
  // PENDING_IO means we're waiting on async.
  Status ReadItem();
  void AdvanceItem();
  void AdvanceBytesRead(int result);
  void ReadBytesItem(const BlobDataItem& item, int bytes_to_read);
  BlobReader::Status ReadFileItem(FileStreamReader* reader, int bytes_to_read);
  void DidReadFile(int result);
  void DeleteItemReaders();
  Status ReadReadableDataHandle(const BlobDataItem& item, int bytes_to_read);
  void DidReadReadableDataHandle(int result);
  void DidReadItem(int result);
  void DidReadSideData(StatusCallback done,
                       int expected_size,
                       int result,
                       mojo_base::BigBuffer data);
  int ComputeBytesToRead() const;
  int BytesReadCompleted();

  // Returns a FileStreamReader for a blob item at |index|.
  // If the item at |index| is not of type kFile this returns nullptr.
  FileStreamReader* GetOrCreateFileReaderAtIndex(size_t index);
  // If the reader is null, then this basically performs a delete operation.
  void SetFileReaderAtIndex(size_t index,
                            std::unique_ptr<FileStreamReader> reader);
  // Creates a FileStreamReader for the item with additional_offset.
  std::unique_ptr<FileStreamReader> CreateFileStreamReader(
      const BlobDataItem& item,
      uint64_t additional_offset);
  // Returns a DataPipeToSourceStream for a blob item at |Index|.
  // If the item at |index| is not of type kReadableDataHandle this returns
  // nullptr.
  network::DataPipeToSourceStream* GetOrCreateDataPipeAtIndex(size_t index);
  void SetDataPipeAtIndex(
      size_t index,
      std::unique_ptr<network::DataPipeToSourceStream> pipe);
  std::unique_ptr<network::DataPipeToSourceStream> CreateDataPipe(
      const BlobDataItem& item,
      uint64_t additional_offset);

  void RecordBytesReadFromDataHandle(int item_index, int result);

  std::unique_ptr<BlobDataHandle> blob_handle_;
  std::unique_ptr<BlobDataSnapshot> blob_data_;
  std::unique_ptr<FileStreamReaderProvider> file_stream_provider_for_testing_;
  scoped_refptr<base::TaskRunner> file_task_runner_;
  base::Optional<mojo_base::BigBuffer> side_data_;

  int net_error_;
  bool item_list_populated_ = false;
  std::vector<uint64_t> item_length_list_;

  scoped_refptr<net::DrainableIOBuffer> read_buf_;

  bool total_size_calculated_ = false;
  uint64_t total_size_ = 0;
  uint64_t remaining_bytes_ = 0;
  size_t pending_get_file_info_count_ = 0;
  std::map<size_t, std::unique_ptr<FileStreamReader>> index_to_reader_;
  std::map<size_t, std::unique_ptr<network::DataPipeToSourceStream>>
      index_to_pipe_;
  size_t current_item_index_ = 0;
  uint64_t current_item_offset_ = 0;

  bool io_pending_ = false;

  net::CompletionOnceCallback size_callback_;
  net::CompletionOnceCallback read_callback_;

  SEQUENCE_CHECKER(sequence_checker_);

  base::WeakPtrFactory<BlobReader> weak_factory_{this};
  DISALLOW_COPY_AND_ASSIGN(BlobReader);
};

}  // namespace storage
#endif  // STORAGE_BROWSER_BLOB_BLOB_READER_H_

redpajama_set_name: RedPajamaGithub


text:
As tech companies begin layoffs, H-1B visa holders are in jeopardy
By Zarish Sajid On Dec 4, 2022, 1:04 am 33 0
Thousands of H-1B employees have been employed by the largest technology companies for many years. However, as a possible recession approaches, many companies are beginning to lay off workers, putting those with this visa in a difficult position, according to CNBC.
Twitter, Meta, Amazon, and Cisco have all announced their intention to lay off thousands of employees. Meta has reportedly let go of 11,000 employees, while Twitter has reportedly let go of 7,500. Numerous smaller startups are also engaging in significant staff reductions. Those H-1B employees targeted by HR may have sixty days to find a new job; if they fail, they will be forced to leave the country. However, depending on their circumstances, an H-1B worker may apply for a different visa, such as a student visa.
Throughout the year, the demand for H-1B visas in the technology sector was observed despite growing economic uncertainty. US Citizenship and Immigration Services reports that for the fiscal year 2023, employees filed 483,000 H-1B registrations, a 57% increase over the fiscal year 2022. (USCIS). Approximately 127,600 registrations were picked by lottery.
Nonetheless, a recession may reduce both this demand and the desire of some foreign workers to work in the American IT industry.
During an interview with Bloomberg Law, Ali Brodie, a partner at Fox Rothschild LLP, pondered, "Will this have a chilling effect on the tech sector by making it less appealing to highly skilled and talented foreign workers?" "Will it relocate these foreign workers away from America? That is the question we must investigate."
Anyone who opposes the H-1B system will welcome a decline in the visa demand. Critics assert that the visa is abused to obtain low-cost skilled labor, particularly by consulting and business service firms that outsource their H-1B employees to other companies.
Both the Trump and Biden administrations proposed reforms and restrictions to the H-1B program, but neither could completely revamp the system.
The H-1B employer-employee relationship could be redefined under a long-delayed USCIS plan (now scheduled for May 2023) that would also update regulations regarding employer site visits and F-1 students transferring to H-1B status.
Those H-1B workers affected by the most recent round of layoffs will need to work quickly to find a new position; however, given the current low unemployment rate in the technology industry, they may not be able to do so within the allotted 60 days.
H- IB visaIT companiesUSA
Zarish Sajid 855 posts 0 comments
A journalist with a passion for writing. Zarish has a strong grip on feminism, environmental crisis, and social justice issues. Being an avid reader who enjoys classic literature, she loves to weave her stories with the inquisitiveness of a reader and the critical thinking of a writer.
She believes that writing is similar to performing arts. A writer has to stage a performance by diving deep into the scene without overdoing the optics. You can connect with her here: Twitter: @SajidZarish or on Facebook: https://www.facebook.com/zarishsajid.6
Lady Diana wanted to convert to Islam, Humayun Saeed
US labels Pakistan and China over religious freedom concerns
Azad is 'Mir Jafar' and 'vote-cutter' propped by BJP, says…


In this post, I will show you upgradation process for Windows Server 2008 R2 Datacenter to Windows Server 2012 R2 Datacenter step by step. Before going to upgrade Windows Server 2008 R2 Datacenter to Windows Server 2012 R2 Datacenter, we have to verify exiting system hardware is supported or not for upgradation of Windows Server 2012 R2. We make sure Windows Server 2012 R2 must meet with minimum hardware requirements. if not, we are unable to upgrade Windows Server 2008 R2 to Windows Server 2012 R2.
Download the Windows Server 2012 R2 ISO Image and mount the ISO Image to CD/DVD, then start the upgradation by click Windos Server 2012 R2 Run setup.exe.
As I verified in Google, I found that, we cannot upgrade Windows Server 2008 R2 Datacenter to Windows Server 2012 R2 Datacenter directly. We can upgrade Windows Server 2008 R2 with SP1 to Windows Server 2012 R2. please find the below table for your reference.
Verify that Windows Server 2008 R2 Datacenter is upgraded to Windows Server 2012 R2 Datacenter edition successfully with out any issues. I hope this is informative for you. Thanks for reading my post……! If you feel worth please share it in social media.


In honor of Presidents Day, try a drinking experience that harkens back to the days of yore? Head to these historical NYC bars, where you might just find the ghosts of Old New York on the bar stool next to you. By Selena Ricks-Good.
Located in a building erected in 1794, Bridge Cafe is NYC's "oldest drinking establishment," and was a bar almost a century before the Brooklyn Bridge was built, which towers above. The saloon has a seedy past of gambling, pirates and prostitution, and was a speakeasy during Prohibition. Legend has it that Ms. Gallus Mag, a 6-foot-tall Irish bouncer, would bite or cut off the ears of misbehaving patrons and pickle them for posterity on shelves above the bar. Try not to think about that as you peruse the menu, known for soft-shell crabs and whiskey, at the well-worn oak wood bar.
A gritty former boarding house turned watering hole for longshoremen, this landmark bar was known for many years as a place for smugglers, gambling and prostitution. The house was built in 1817 for African American Revolutionary War veteran James Brown, said to be an aide to George Washington. In 1977, the bar's new owners didn't want to deal with the hassle of getting Landmark Commission approval for a new sign–so the neon "Bar" sign was painted to read "Ear." Today, the bar is a local hangout for artists and musicians, but is said to be haunted by Mickey, a sailor who died when hit by a car just outside the bar.
While there's controversy over whether McSorley's opened in 1854, as its sign claims, or a few years later, the Irish taproom is a shrine to its storied past. There's sawdust on the floor, yellowing portraits of the likes of Franklin Delano Roosevelt on the walls, and a chandelier made of poultry wishbones said to represent soldiers who never returned from the Great War. Choose from either light or dark house-made beer, served two mugs at a time, and soak up the history.
Established in 1864, Pete's Tavern claims to hold the much-disputed title for NYC's "oldest continually operating restaurant and bar." The ornate dark wood bar is a low-key place for a pint, including the house special, 1864 Original House Ale. O. Henry, who lived nearby at 55 Irving Place, wrote "The Gift of the Magi" here in a booth to the right of the front door.
In 1868, this Irish saloon opened on the shores of the Hudson River serving pints to dock workers–back then, there was no 12th Avenue. During Prohibition, the third floor was turned into a speakeasy. Refurbished by new owners in 2004, the original mahogany bar is carved from one single tree remains intact, as well as the tin ceilings and tile floor. Over the years, waiters have claimed the bar is haunted by an Irish girl and a Confederate soldier.
For the latest on NYC's bar and nightlife scene, follow us on Twitter! Selena Ricks-Good writes about drinks and produces events as The Dizzy Fizz.


Setting benchmarks — and adhering to them — allows an organization's board and management to know where the business stands both financially and operationally and then take appropriate action to meet strategic goals and objectives. Strong organizations don't operate in a vacuum; they set benchmarks.
For-profit organizations are driven by quantifiable financial benchmarks — stock price, revenue, and net income — that allow them to track success. Not-for-profit organizations, which focus on mission and are often highly dependent on donations, have different motivations and expectations. So while setting financial benchmarks is an important undertaking for both types of entities, "traditional financial benchmarks usually don't tell the whole story for a not-for-profit organization," says Tanya Hahn, senior vice president at Lancaster Pollard.
Unlike for-profits, the resource mix for not-for-profits includes gifts and donations, people skills, volunteers on the board and working in the facilities, and employees who are often paid less than those in the for-profit world. When assessing long-term success, then, not-for-profits are often driven by benchmarks that address more subjective, qualitative things — the mission, the board and management, and the strategic plan.
Financial benchmarks help you compare your organization to your peers and to industry standards, thereby evaluating your success outside the vacuum of your own unique operation. Benchmarks provide a way to convey financial results, with clarity, to board members, management, employees, residents, and community stakeholders.
You can benchmark a lot of things: cash in the bank, revenues generated, campus population, occupancy statistics, payer mixes, etc. The primary financial benchmark that a not-for-profit senior living organization should assess, though, is days cash on hand.
Then, how much debt is the organization taking on? Considering amortization and entrance-fee refunds, the typical CCRC tends to have a fairly high debt service ratio — in the 70% to 80% level, according to Hahn. So if you're taking on a new project, the question becomes: How much leverage does that create?
And what about the external factors? Is the market increasing or decreasing? How are you dealing with competitors coming into the area? What is the reimbursement environment in your state? "You can't change much about external factors," says Hahn, "but you must manage around them." If the state lowers the Medicaid reimbursement, for example, you must have the resources to deal with that for a period of time.
At the end of the day, the goal of a not-for-profit senior living organization is to continue its mission in perpetuity. Non-traditional benchmarks — which revolve around the mission and the people who passionately believe in it — measure non-financial strengths and weaknesses.
"Through mission-centered benchmarks (as opposed to number-centered benchmarks), you're confirming your story to the community, to residents, and to prospective residents," says Hahn. Is the administrator proactive? Do board members understand the market you're serving? Do they realize they must be stewards of the organization's assets from a long-term sustainability standpoint? Is the staff motivated? Do you have the right people in the right positions? What are you offering that allows them to confirm their commitment to the organization (flexible hours, scholarship opportunities, etc.)? Is the facility perceived by residents, family members, and the community as well-run and well-maintained?
A for-profit operation competing in the same community may have newer buildings and fancier grounds than a neighboring not-for-profit facility, but is its care that much better? Maybe not. Keeping buildings and the grounds well maintained and making the environment pleasant simply requires attention to detail and caring. Is the building clean? Is the signage easy to read? Is the lawn cut? Do the CEO, administrator, and head nurse seem to care? Is there a genuine connection between staff and residents? Between staff and family members?
Continuing Care Accreditation Commission (CCAC), Standard & Poor's, and Fitch all evaluate and rate senior living organizations, but very few of the 2,000-plus not-for-profit senior living communities in the country are rated. Some state organizations evaluate health-care organizations, too, although mostly on the hospital side. Standard & Poor's rates roughly 90 different not-for-profit senior living organizations and differentiates its medians by single-site vs. multi-site and contract type. Fitch rates about 70 organizations and analyzes its medians by contract type. But organizations are all different, Hahn warns, so when picking a median, know the limitations of the one to which you're comparing your organization.
CCAC medians include 310 organizations differentiated by single-site vs. multi-site and contract type. About 40% of those organizations are rated, so there will be some duplication with the rating agency data.
Hahn also cautions providers about depending too much on the medians supplied by the rating agencies when evaluating financial benchmarks. "The organizations included in the Standard & Poor's and Fitch medians are highly concentrated in the Midwest," she says. "That's a limitation if your facility is located in New England or California; 18 states have no representation. And there are some slight differences in calculating the ratios." Nevertheless, the medians are a helpful tool for setting benchmarks and a useful gauge when evaluating them.
If you have $5 million in cash and are planning a $5 million project, should you spend the cash or borrow? Investing your money at a 6% return and then borrowing at a 5% rate provides a 1% positive spread. By using the invested cash to pay off the loan over 25 years, the organization would end up with $2 million in the bank. By contrast, spending the $5 million eliminates the opportunity to earn any money on it.
Certainly the first step to building liquidity is hiring independent money managers who understand the not-for-profit world, as well as how changes in market value could affect covenants on the organization's debt.
If you're putting cash on the balance sheet, and the board has designated it for long-term capital needs or a rainy day fund, she suggests that you put together a long-term investment policy and prudently invest the money.
Also, many organizations with debt on the balance sheet – particularly variable-rate debt – may worry about rising interest rates and having to reinvest. Interest rates on their investments may also be going up; the spread may remain the same. Debt should not be too highly leveraged relative to accumulated net assets, though, because that reduces your wiggle room.


Dorothy Bar-Adon (August 2, 1907 – August 7, 1950) was an American-born Israeli journalist. Her early experience as a correspondent was gained on ''The Atlantic City'' [New Jersey] ''Press''. From her immigration to Palestine in 1933 until her death she worked as a journalist for ''The Palestine Post'' (later to become ''The Jerusalem Post''), covering a wide range of international and domestic issues.


text:
Food & Eating
Botanical Remedy Shows Promise for Prostate Cancer
Researchers say a non-toxic, plant-based formula — with no side effects — could help suppress tumor growth in aggressive, treatment-resistant prostate cancer. So what's the catch?
Allison Takeda
FRIDAY, March 30, 2012 — Imagine a non-toxic, botanical treatment that can stop the spread of cancer with none of the unwanted side effects common to chemotherapy or radiation. It sounds too good to be true, we know — but researchers say it's not out of the realm of possibility.
In fact, they've been studying such a treatment for prostate cancer — a formula made of botanical extracts, phytonutrients, botanically enhanced medicinal mushrooms, and antioxidants. And though testing is still in the early stages, the findings thus far have been promising.
According to a new study published in The International Journal of Oncology, the formula could help control hard-to-treat prostate tumors — for example, those that are aggressive and androgen-independent, or resistant to hormone-deprivation therapy. Researchers at Indiana University's Methodist Research Institute implanted such tumors into mice and then orally administered the formula to the rodents at various doses. They found that it successfully suppressed tumor growth and spread without any toxic side effects, even when given in large amounts. More specifically, it limited growth by 27 percent and helped prevent metastasis by blocking some of the genes responsible for the spread of cancer.
These results support earlier data from Indiana University, as well as research from Columbia University, which found that the mixture could inhibit the growth of abnormal cells and even stimulate cell death in aggressive prostate tumors.
Prostate Problems: Could Your Symptoms Be Prostate Cancer? 11 Signs You Might Have Prostate Problems
Treating Nonmetastatic Castration-Resistant Prostate Cancer
"This dietary supplement is a natural compound for the possible therapy of human hormone refractory (independent) prostate cancer," lead scientist Daniel Silva, MD, said in a press release, adding that other research had demonstrated the formula's effectiveness as well. Unlike those previous trials, however, which were done in vitro (meaning in glass, or in test tubes and petri dishes), Silva's involved living organisms and was able to show the treatment's impact in a more complex biological environment.
"This study is a milestone in the research of this formula, demonstrating its safety and effectiveness in treating human prostate cancer in an animal model," added Isaac Eliaz, MD, the integrative medicine expert who invented the formula. "These positive results offer a significant contribution to prostate cancer research and add to the growing body of published data substantiating the role of natural compounds in the treatment of prostate cancer."
For more information about alternative cancer treatments, visit our Cancer Center, and follow @CancerFacts and @WomensCancer on Twitter.
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Prostate Cancer Screening, Diagnosis, and Treatment Have Declined: Study
Prostate Cancer Healthcare Team
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Large Study Finds No Link Between Use of Talcum Powder in Genital Area and Ovarian Cancer
The possible association between genital powder use and ovarian cancer has generated controversy for almost a decade. Now the largest study to date says...
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At-home testing for high-risk cancer gene mutations like BRCA1 and BRCA2 have been made readily available to consumers, but experts caution they may not...
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When Linda Boyed was diagnosed with bile duct caner, two doctors told her there was little they could do. She got a third opinion, which resulted in the...
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San Antonio Breast Cancer Symposium: Immunotherapy May Help Some Women With Triple Negative Breast Cancer, and More From Day 2 of the Conference
Immunotherapy, breast cancer prevention, and luminal B breast cancer were among the news items on day two of the San Antonio breast cancer conference....
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February's Games with Gold game on Xbox One, #IDARB, has been made free a few days ahead of schedule.
#IDARB is a couch co-op, arcade sports game. It's also February's free Games with Gold game on Xbox One.
But you don't need to wait until February. The game is available right now through the Xbox Store for anyone with an active Gold subscription.
It's a small download too, at just 402MB.


where PSA(f) is in m∕s2, a = -5.08210, b = 2.06210, c = -0.11966, d = -0.00319, e = 0.00488, S = -0.01145 and σ = 0.32257 for borehole stations (S applies for stations at 200m) and a = -4.884, b = 2.18080, c = -0.12964, d = -0.00397, e = 0.01226, SB = 0.16101, SC = 0.27345, SD = 0.45195 and σ = 0.35325 for surface stations.
Do not report coefficients of these models.
360 < V s,30 < 800m∕s. Use coefficient SB.
180 < V s,30 < 360m∕s. Use coefficient SC.
V s,30 < 180m∕s. Use coefficient SD.
Use data from boreholes to reduce influence of nonlinear site effects for investigating magnitude-dependent decay. Also derive models using surface records.
Only use data from < 100km.
Only retain events with depth < 25km to exclude subduction earthquakes.
Note relatively good magnitude-distance coverage.
Visually inspect records to retain only main event if multiple events recorded and to check for glitches. Bandpass Butterworth (four poles and two passes) filter records with cut-offs 0.25 and 25Hz. Longest usable period of model is less than 3s due to filtering.
Derive equations using data from small (Mw ≤ 5) earthquakes (3376 records from 310 events) and large (Mw ≥ 5) earthquakes (518 records from 27 events) to examine ability of models to predict ground motions outside their magnitude range of applicability. Find ground motions from small events attenuate faster than from large events. Predict ground motions for Mw 4.0, 5.0 and 6.5 and 10, 30 and 99km. Find overestimation of ground motions for Mw4.0 using model derived using data from Mw ≥ 5 and overestimation of ground motions for Mw6.5 using model derived using data from Mw ≤ 5. Predictions for Mw5.0 are similar for both models. Also compare predictions from both models and observations for Mw4.1, 4.6, 5.2, 5.7, 6.5 and 7.3 and find similar results.
Also derive models for 11 magnitude ranges: 4.0–4.2, 4.2–4.4, 4.4–4.6, 4.6–4.8, 4.8–5.0, 5.0–5.2, 5.2–5.4, 5.6–5.8, 5.8–6.8 and 6.8–7.3. Compare predictions with observations for each magnitude range and find good match. Find that decay rate depends on Mw with faster decay for small events. Plot σs from each model w.r.t. Mw and find that it has a negative correlation with Mw.
Examine residuals w.r.t. distance. Find slight increase at large distances, which relate to magnitude dependency of attenuation.
Note that goal of analysis was not to compete with existing models but to compare magnitude dependency of ground motions at depth and surface.
Examine residuals w.r.t. distance and magnitude of final model. Find no trends.
Find that σs for surface motions are larger (by about 9%) than those for motions at depth.


Fake websites & fraud: The latest on Equifax.
Be careful where you navigate on the internet to get information on the breach.
There was a potential issue when someone created a fake website – "securityequifax2017.com" as opposed the actual site that Equifax set up for information on the breach – "equifaxsecurity2017.com." This easily could have been a malicious scammer trying to obtain consumers' information; however, luckily it was a software engineer who created the site to show how easy it is to impersonate Equifax's response website. Notably, as of September 25th, there have been over 1000 fake websites launched.
To compound the issue, Equifax actually posted links to this fake site instead of their own on via Twitter on September 20th. Yet another embarrassing bungling by the company that caused the massive issue.
Active Monitoring, Credit Fraud Alert, or Credit Freeze?
Active Monitoring – You can take advantage of the free Equifax monitoring, as well as getting your free annual credit report, and signing up for www.creditkarma.com and www.creditsesame.com. This will allow you to keep tabs on your existing accounts, as well as anything that occurs involving your credit.
This also removes you from marketing lists for prescreened credit offers for five years unless you ask them to put your name back on the list.
Creditors that rely on a credit report or score in granting credit will likely refuse to offer the same service because of the lack of information.
It only protects against new accounts being opened, as it does not apply to actions on existing accounts, so you must still regularly review your credit reports.
If you freeze your credit, you must undo the freeze ("thawing") to allow anyone to access your credit or obtain your score when you need it to occur (applying for a mortgage, car loan, an apartment, a job, for insurance, etc.). You are charged for that lifting (again $5-$10).
So which course of action do you take? It is a judgment call for each person based on their circumstances and needs. For instance, if you need to access your credit regularly, the credit freeze will be time-consuming and expensive. The fraud alert is a free easier step, but is initially only good for 90 days and must be renewed every 90 days unless you take the additional steps necessary for the extended fraud alert. It could be a good middle-of-the-road step. If you regularly monitor your accounts, you may want to just keep tabs on things.
Stay vigilant by regularly checking your credit reports. If you have any questions on the information provided, or discover any incorrect information or fraudulent activity on your credit reports, contact us.


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