text:
\section{Introduction}

Given an input sequence, {\it segmentation} is the problem of identifying and assigning tags to its subsequences.
Many natural language processing (NLP) tasks can be cast into the segmentation problem, like named entity recognition \cite{okanohara-EtAl:2006:COLACL}, opinion extraction \cite{yang-cardie:2012:EMNLP-CoNLL}, and Chinese word segmentation \cite{andrew:2006:EMNLP}.
Properly representing {\it segment} is critical for good segmentation performance.
Widely used sequence labeling models like conditional random fields \cite{Lafferty:2001:CRF:645530.655813} represent the contextual information of the segment boundary as a proxy to entire segment and achieve segmentation by labeling input units (e.g. words or characters) with boundary tags.
Compared with sequence labeling model, models that directly represent segment are attractive because they are not bounded by local tag dependencies and can effectively adopt segment-level information.
Semi-Markov CRF (or semi-CRF) \cite{NIPS2005_427} is one of the models that directly represent the entire segment.
In semi-CRF, the conditional probability of a semi-Markov chain on the input sequence is explicitly modeled, whose each state corresponds to a subsequence of input units, which makes semi-CRF a natural choice for segmentation problem.

However, to achieve good segmentation performance, conventional semi-CRF models require carefully hand-crafted features to represent the segment.
Recent years witness a trend of applying neural network models to NLP tasks.
The key strengths of neural approaches in NLP are their ability for modeling the compositionality of language and learning distributed representation from large-scale unlabeled data.
Representing a segment with neural network is appealing in semi-CRF because various neural network structures \cite{Hochreiter:1997:LSM:1246443.1246450} have been proposed to compose sequential inputs of a segment and the well-studied word embedding methods \cite{DBLP:journals/corr/MikolovSCCD13} make it possible to learn entire segment representation from unlabeled data.

In this paper, we combine neural network with semi-CRF and make a thorough study on the problem of representing a segment in neural semi-CRF.
\textcite{DBLP:journals/corr/KongDS15} proposed a segmental recurrent neural network (SRNN) which represents a segment by composing input units with RNN.
We study alternative network structures besides the SRNN.
We also study segment-level representation using {\it segment embedding} which encodes the entire segment explicitly.
We conduct extensive experiments on two typical NLP segmentation tasks: named entity recognition (NER) and Chinese word segmentation (CWS).
Experimental results show that our concatenation alternative achieves comparable performance with the original SRNN but runs 1.7 times faster and our neural semi-CRF greatly benefits from the segment embeddings.
In the NER experiments, our neural semi-CRF model with segment embeddings achieves an improvement of 0.7 F-score over the baseline and the result is competitive with state-of-the-art systems.
In the CWS experiments, our model achieves more than 2.0 F-score improvements on average.
On the PKU and MSR datasets, state-of-the-art F-scores of 95.67\% and 97.58\% are achieved respectively.
We release our code at \url{https://github.com/ExpResults/segrep-for-nn-semicrf}.

\section{Problem Definition}

Figure \ref{fig:ne-and-cws} shows examples of named entity recognition and Chinese word segmentation.
For the input word sequence in the NER example, its segments ({\it ``Michael Jordan'':PER, ``is'':NONE, ``a'':NONE, ``professor'':NONE, ``at'':NONE, ``Berkeley'':ORG}) reveal that ``Michaels Jordan'' is a person name and ``Berkeley'' is an organization.
In the CWS example, the subsequences (\begin{CJK*}{UTF8}{gkai}``浦东/Pudong'', ``开发/development'', ``与/and'', ``建设/construction''\end{CJK*}) of the input character sequence are recognized as words.
Both NER and CWS take an input sequence and partition it into disjoint subsequences.

\begin{figure}[t]
\centering
\includegraphics[width=0.9\columnwidth,trim={0cm 0.4cm 7.8cm 12cm},clip]{necws}
\caption{Examples for named entity recognition (above) and Chinese word segmentation (below).}
\label{fig:ne-and-cws}
\end{figure}

Formally, for an input sequence $\mathbf{x}=( x_1, .., x_{|\mathbf{x}|} )$ of length $|\mathbf{x}|$, let $x_{a:b}$ denote its subsequence $(x_a,...,x_b)$.
A {\it segment} of $\mathbf{x}$ is defined as $(u, v, y)$ which means the subsequence $x_{u:v}$ is associated with label $y$.
A {\it segmentation} of $\mathbf{x}$ is a {\it segment} sequence $\mathbf{s} = (s_1,..,s_p)$, where $s_j=(u_j,v_j,y_j)$ and $u_{j+1}=v_j+1$.
Given an input sequence $\mathbf{x}$, the {\it segmentation} problem can be defined as the problem of finding $\mathbf{x}$'s most probable {\it segment} sequence $\mathbf{s}$.

\begin{figure*}[t]
\begin{subfigure}[b]{0.241\textwidth}
\centering
\includegraphics[width=\columnwidth,trim={3cm 2.8cm 5cm 5cm},clip]{std}
\caption{semi-CRF}\label{fig:std}
\end{subfigure}
~
\begin{subfigure}[b]{0.241\textwidth}
\centering
\includegraphics[width=\columnwidth,trim={3cm 2.8cm 5cm 3.9cm},clip]{rnn}
\caption{SRNN}\label{fig:rnn}
\end{subfigure}
~
\begin{subfigure}[b]{0.241\textwidth}
\centering
\includegraphics[width=\columnwidth,trim={3cm 2.8cm 5cm 3.9cm},clip]{cnn}
\caption{SCNN}\label{fig:cnn}
\end{subfigure}
~
\begin{subfigure}[b]{0.241\textwidth}
\centering
\includegraphics[width=\columnwidth,trim={3cm 2.8cm 5cm 3.9cm},clip]{concate}
\caption{SCONCATE}\label{fig:concate}
\end{subfigure}
\caption{An illustration for the semi-CRF, SRNN, SCNN and SCONCATE.
In these figures, circles represent the inputs, blue rectangles represent {\it factors} in graphic model and yellow rectangles represent generic nodes in the neural network model.}
\label{fig:semi-crf-vs-srnn}
\end{figure*}

\section{Neural Semi-Markov CRF}

Semi-Markov CRF (or semi-CRF, Figure \ref{fig:std}) \cite{NIPS2005_427} models the conditional probability of $\mathbf{s}$ on $\mathbf{x}$ as
\[
p(\mathbf{s}|\mathbf{x})=\frac{1}{Z(\mathbf{x})}\exp\{W \cdot G(\mathbf{x},\mathbf{s})\}
\]
where $G(\mathbf{x},\mathbf{s})$ is the feature function, $W$ is the weight vector and $Z(\mathbf{x})=\sum_{\mathbf{s'}\in \mathbf{S}} \exp\{W \cdot G(\mathbf{x}, \mathbf{s'})\}$ is the normalize factor of all possible {\it segmentations} $\mathbf{S}$ over $\mathbf{x}$.

By restricting the scope of feature function within a segment and ignoring label transition between segments (0-order semi-CRF), $G(\mathbf{x},\mathbf{s})$ can be decomposed as $G(\mathbf{x}, \mathbf{s})=\sum_{j=1}^p g(\mathbf{x},s_j)$ where $g(\mathbf{x},s_j)$ maps segment $s_j$ into its representation.
Such decomposition allows using efficient dynamic programming algorithm for inference.
To find the best segmentation in semi-CRF, let $\alpha_j$ denote the best segmentation ends with $j$\textsuperscript{th} input and $\alpha_j$ is recursively calculated as
\[\mathbf{\alpha}_j=\max_{l=1..L,y} \Psi(j-l,j,y) + \mathbf{\alpha}_{j-l-1}\]
where $L$ is the maximum length manually defined and $\Psi(j-l,j,y)$ is the transition weight for $s=(j-l,j,y)$ in which $\Psi(j-l,j,y)=W\cdot g(\mathbf{x}, s)$.

Previous semi-CRF works \cite{NIPS2005_427,okanohara-EtAl:2006:COLACL,andrew:2006:EMNLP,yang-cardie:2012:EMNLP-CoNLL} parameterize $g(\mathbf{x},s)$ as a sparse vector, each dimension of which represents the value of corresponding feature function.
Generally, these feature functions fall into two types: 1) the {\it CRF style features} which represent input unit-level information such as ``the specific words at location $i$''  2) the {\it semi-CRF style features} which represent segment-level information such as ``the length of the segment''.

\textcite{DBLP:journals/corr/KongDS15} proposed the segmental recurrent neural network model (SRNN, see Figure \ref{fig:rnn}) which combines the semi-CRF and the neural network model.
In SRNN, $g(\mathbf{x}, s)$ is parameterized as a bidirectional LSTM (bi-LSTM).
For a segment $s_j=(u_j,v_j,y_j)$, each input unit $x$ in subsequence $(x_{u_j},..,x_{v_j})$ is encoded as {\it embedding} and fed into the bi-LSTM.
The rectified linear combination of the final hidden layers from bi-LSTM is used as $g(\mathbf{x}, s)$.
\textcite{DBLP:journals/corr/KongDS15} pioneers in representing a segment in neural semi-CRF.
Bi-LSTM can be regarded as ``neuralized'' {\it CRF style features} which model the input unit-level compositionality.
However, in the SRNN work, only the bi-LSTM was employed without considering other input unit-level composition functions.
What is more, the {\it semi-CRF styled} segment-level information as an important representation was not studied.
In the following sections, we first study alternative input unit-level composition functions (\ref{sec:alt-inp-rep}).
Then, we study the problem of representing a segment at segment-level (\ref{sec:seg-rep}).

\subsection{Alternative Seg-Rep. via Input Composition}\label{sec:alt-inp-rep}

\subsubsection{Segmental CNN}
Besides recurrent neural network (RNN) and its variants, another widely used neural network architecture for composing and representing variable-length input is the convolutional neural network (CNN) \cite{Collobert:2011:NLP:1953048.2078186}.
In CNN, one or more filter functions are employed to convert a fix-width segment in sequence into one vector.
With filter function ``sliding'' over the input sequence, contextual information is encoded.
Finally, a pooling function is used to merge the vectors into one.
In this paper, we use a filter function of width 2 and max-pooling function to compose input units of a segment.
Following SRNN, we name our CNN segment representation as SCNN (see Figure \ref{fig:cnn}).

However, one problem of using CNN to compose input units into segment representation lies in the fact that the max-pooling function is insensitive to input position.
Two different segments sharing the same vocabulary can be treated without difference.
In a CWS example, \begin{CJK*}{UTF8}{gkai}``球拍卖'' (racket for sell) and ``拍卖球'' (ball audition)\end{CJK*} will be encoded into the same vector in SCNN if the vector of \begin{CJK*}{UTF8}{gkai}``拍卖''\end{CJK*} that produced by filter function is always preserved by max-pooling.

\subsubsection{Segmental Concatenation}
Concatenation is also widely used in neural network models to represent fixed-length input.
Although not designed to handle variable-length input, we see that in the inference of semi-CRF, a maximum length $L$ is adopted, which make it possible to use padding technique to transform the variable-length representation problem into fixed-length of $L$.
Meanwhile, concatenation preserves the positions of inputs because they are directly mapped into the certain positions in the resulting vector.
In this paper, we study an alternative concatenation function to compose input units into segment representation, namely the SCONCATE model (see Figure \ref{fig:concate}).
Compared with SRNN, SCONCATE requires less computation when representing one segment, thus can speed up the inference.

\subsection{Seg-Rep. via Segment Embeddings}\label{sec:seg-rep}

For segmentation problems, a segment is generally considered more informative and less ambiguous than an individual input.
Incorporating segment-level features usually lead performance improvement in previous semi-CRF work.
Segment representations in Section \ref{sec:alt-inp-rep} only model the composition of input units.
It can be expected that the segment embedding which encodes an entire subsequence as a vector can be an effective way for representing a segment.

In this paper, we treat the segment embedding as a lookup-based representation, which retrieves the embedding table with the surface string of entire segment.
With the entire segment properly embed, it is straightforward to combine the segment embedding with the composed vector from the input so that multi-level information of a segment is used in our model (see Figure \ref{fig:with-seg}).
However, how to obtain such embeddings is not a trivial problem.

\begin{figure}[t]
\centering
\includegraphics[width=0.7\columnwidth,trim={2cm 2.8cm 2cm 3.9cm},clip]{with_seg}
\caption{Our neural semi-CRF model with segment representation from input composition and segment embeddings.}\label{fig:with-seg}
\end{figure}

A natural solution for obtaining the segment embeddings can be collecting all the ``correct'' segments from training data into a lexicon and learning their embeddings as model parameters.
However, the in-lexicon segment is a strong clue for a subsequence being a correct segment, which makes our model vulnerable to overfitting.
Unsupervised pre-training has been proved an effective technique for improving the robustness of neural network model \cite{Erhan:2010:WUP:1756006.1756025}.
To mitigate the overfitting problem, we initialize our segment embeddings with the pre-trained one.

Word embedding gains a lot of research interest in recent years \cite{DBLP:journals/corr/MikolovSCCD13} and is mainly carried on English texts which are naturally segmented.
Different from the word embedding works, our segment embedding requires large-scale segmented data, which cannot be directly obtained.
Following \textcite{wang-EtAl:2011:IJCNLP-2011} which utilize automatically segmented data to enhance their model, we obtain the auto-segmented data with our neural semi-CRF baselines (SRNN, SCNN, and SCONCATE) and use the auto-segmented data to learn our segment embeddings.

Another line of research shows that machine learning algorithms can be boosted by ensembling {\it heterogeneous} models.
Our neural semi-CRF model can take knowledge from heterogeneous models by using the segment embeddings learned on the data segmented by the heterogeneous models.
In this paper, we also obtain the auto-segmented data from a conventional CRF model which utilizes hand-crafted sparse features.
Once obtaining the auto-segmented data, we learn the segment embeddings in the same with word embeddings.

\begin{figure}[t]
\centering
\includegraphics[width=0.8\columnwidth,trim={0cm 0cm 3.5cm 11.2cm},clip]{tran}
\caption{An example for fine-tuning decreases the generalization power of pre-trained segment embedding.
``1994\_World\_Cup'' does not occur in the training data and its similarity with ``1998\_World\_Cup'' is broken because ``1998\_World\_Cup'' is tuned.}\label{fig:wo-ft}
\end{figure}

A problem that arises is the fine-tuning of segment embeddings.
Fine-tuning can learn a task-specific segment embeddings for the segments that occur in the training data, but it breaks their relations with the un-tuned out-of-vocabulary segments.
Figure \ref{fig:wo-ft} illustrates this problem.
Since OOV segments can affect the testing performance, we also try learning our model without fine-tuning the segment embeddings.

\subsection{Model details}

In this section, we describe the detailed architecture for our neural semi-CRF model.

\subsubsection{Input Unit Representation}
Following \textcite{DBLP:journals/corr/KongDS15}, we use a bi-LSTM to represent the input sequence.
To obtain the input unit representation, we use the technique in \textcite{dyer-EtAl:2015:ACL-IJCNLP} and separately use two parts of input unit embeddings: the pre-trained embeddings $E^p$ without fine-tuning and fine-tuned embeddings $E^t$.
For the $i$th input, $E^p_i$ and $E^t_i$ are merged together through linear combination and form the input unit representation
\[I_i=\textsc{ReLU}(W^\mathcal{I} [E_i^p ; E_i^t] + b^\mathcal{I})\]
where the notation of $W[X_1;..;X_n]$ equals to $X_1,..,X_n$'s linear combination $W_1 X_1 + .. + W_n X_n$ and $b^I$ is the bias.
After obtaining the representation for each input unit, a sequence $(I_1,...,I_{|\mathbf{x}|})$ is fed to a bi-LSTM.
The hidden layer of forward LSTM $\overrightarrow{H_i}$ and backward LSTM $\overleftarrow{H_i}$ are combined as \[H_i=\textsc{ReLU}(W^\mathcal{H}[\overrightarrow{H_i};\overleftarrow{H_i}]+b^\mathcal{H})\]
and used as the $i$\textsuperscript{th} input unit's final representation.

\subsubsection{Segment Representation}
Given a segment $s_j=(u_j,v_j,y_j)$, a generic function \textsc{SComp}$(H_{u_j},...,H_{v_j})$ stands for the segment representation that composes the input unit representations $(H_{u_j},..,H_{v_j})$.
In this work, \textsc{SComp} is instantiated with three different functions: SRNN, SCNN and SCONCATE.
Besides composing input units, we also employ the segment embeddings as segment-level representation.
Embedding of the segment $s_j=(u_j,v_j,y_j)$ is denoted as a generic function \textsc{SEmb}$(x_{u_j}...x_{v_j})$ which converts the subsequence $(x_{u_j},...,x_{v_j})$ into its embedding through a lookup table.
At last, the representation of segment $s_j$ is calculated as
\[S_j=\textsc{ReLU}(W^\mathcal{S}[\textsc{SComp}_j;\textsc{SEmb}_j;E^Y_{y_j}]+b^\mathcal{S})\]
where $E^Y$ is the embedding for the label of a segment.

\begin{table}[t]
\small
\centering
\begin{tabular}{r|l}
\hline
fixed input unit embedding $E^p_i$ size      & 100 \\
fine tuned input unit embedding $E^t_i$ size & 32 \\
input unit representation $I_i$ size       & 100 \\
LSTM hidden layer $H_i$ size            & 100 \\
seg-rep via input composition {\sc SComp} & 64 \\
seg-rep via segment embedding {\sc SEmb} & 50 \\
label embedding $E^Y_{y_i}$ size & 20 \\
final segment representation $S_i$ size & 100 \\
\hline
\end{tabular}
\caption{Hyper-parameter settings}
\label{tbl:hyper-parameter}
\end{table}

Throughout this paper, we use the same hyper-parameters for different experiments as listed in Table \ref{tbl:hyper-parameter}.
\subsubsection{Training Procedure}

In this paper, negative log-likelihood is used as learning objective.
We follow \textcite{dyer-EtAl:2015:ACL-IJCNLP} and use stochastic gradient descent to optimize model parameters.
Initial learning rate is set as $\eta_0=0.1$ and updated as $\eta_t=\eta_0/(1+0.1t)$ on each epoch $t$.
Best training iteration is determined by the evaluation score on development data.

\section{Experiment}
We conduct our experiments on two NLP segmentation tasks: named entity recognition and Chinese word segmentation.

\subsection{Dataset and Word Embedding}

For NER, we use the CoNLL03 dataset which is widely adopted for evaluating NER models' performance.
F-score is used as evaluation metric.\footnote{{\tt conlleval} script in CoNLL03 shared task is used.}

For CWS, we follow previous study and use three Simplified Chinese datasets: PKU and MSR from 2\textsuperscript{nd} SIGHAN bakeoff and Chinese Treebank 6.0 (CTB6).
For the PKU and MSR datasets, last 10\% of the training data are used as development data as \cite{pei-ge-chang:2014:P14-1} does.
For CTB6 data, recommended data split is used.
We convert all the double byte digits and letters in the PKU data into single byte.
Like NER, CWS performance is evaluated by F-score.\footnote{{\tt score} script in 2\textsuperscript{nd} SIGHAN bakeoff is used.}

Unlabeled data are used to learn both the input unit embeddings (word embedding for NER, character embedding for CWS) and segment embeddings.
For NER, we use RCV1 data as our unlabeled English data.
For CWS, Chinese gigawords is used as unlabeled Chinese data.
Throughout this paper, we use the word embedding toolkit released by \textcite{ling-EtAl:2015:NAACL-HLT} to obtain both the input unit embeddings and segment embeddings.\footnote{\tt https://github.com/wlin12/wang2vec}

\subsection{Baseline}

We compare our models with three baselines:
\begin{enumerate}
\item {\sc Sparse-CRF}: The CRF model using sparse hand-crafted features.
\item {\sc NN-Labeler}: The neural network sequence labeling model making classification on each input unit.
\item {\sc NN-CRF}: The neural network CRF which models the conditional probability of a label sequence over the input sequence.
\end{enumerate}

BIESO-tag schema is used in all the CRF and sequence labeling models.\footnote{O tag which means OUTSIDE is not adopted in CWS experiments since CWS doesn't involve assigning tags to segments.}
For {\sc Sparse-CRF}, we use the baseline feature templates in \textcite{guo-EtAl:2014:EMNLP2014} for NER and
\textcite{jiang-EtAl:2013:ACL2013}'s feature templates for CWS.
Both {\sc NN-Labeler} and {\sc NN-CRF} take the same input unit representation as our neural semi-CRF models but vary on the output structure and do not explicitly model segment-level information.

\subsection{Comparing Different Input Composition Functions}

\begin{table*}[t]
\small
\centering
\begin{tabular}{r|r||cc | cc cc cc| c}
\hline
 \multicolumn{2}{r||}{} & \multicolumn{2}{c|}{NER}  & \multicolumn{6}{c|}{CWS} & \\
 \multicolumn{2}{r||}{} & \multicolumn{2}{c|}{CoNLL03} & \multicolumn{2}{c}{CTB6} & \multicolumn{2}{c}{PKU} & \multicolumn{2}{c|}{MSR} & \\
 \multicolumn{2}{r||}{\it model} & dev & test & dev & test & dev & test & dev & test & spd \\
\hline
\multirow{3}{*}{\it baseline} & \sc NN-Labeler & 93.03 & 88.62 & 93.70 & 93.06 & 93.57 & 92.99 & 93.22 & 93.79 & \bf 3.30 \\
& \sc NN-CRF &\bf 93.06 &\bf 89.08 & 94.33 & 93.65 & 94.09 & 93.28 & 93.81 & 94.17 & 2.72 \\
\cline{2-11}
& \sc Sparse-CRF & 88.87 & 83.43 &\bf 95.68 &\bf 95.08 &\bf 95.85 &\bf 95.06 &\bf 96.09 &\bf 96.54 & \\
\hline
\multirow{3}{*}{\it neural semi-CRF}& \sc SRNN & 92.97 & 88.63 & 94.56 & 94.06 & 94.86 & 93.91 & 94.38 & 95.21 & 0.62 \\
& \sc SCONCATE & 92.96 & 89.07 & 94.34 & 93.96 & 94.41 & 93.57 & 94.05 & 94.53 & 1.08 \\
& \sc SCNN & 91.53 & 87.68 & 87.82 & 87.51 & 79.64 & 80.75 & 85.04 & 85.79 & 1.46 \\
\hline
\end{tabular}
\caption{The NER and CWS results of the baseline models and our neural semi-CRF models with different input composition functions.
{\it spd} represents the inference speed and is evaluated by the number of tokens processed per millisecond.}
\label{tbl:close-result}
\end{table*}

We first consider the problem of representing segments via composing input units and compare different input composition functions.
Results on NER and CWS data are shown in Table \ref{tbl:close-result}.
From this table, the SRNN and SCONCATE achieve comparable results and perform better than the SCNN.
Although CNN can model input sequence at any length, its invariance to the exact position can be a flaw in representing segments.
The experimental results confirm that and show the importance of properly handling the input position.
Considering SCNN's relatively poor performance, we only study SRNN and SCONCATE in the following experiments.

Comparing with {\sc NN-Labeler}, structure prediction models (NN-CRF and neural semi-CRF) generally achieve better performance.
The best structure prediction model outperforms {\sc NN-Labeler} by 0.4\% on NER and 1.11\% averagely on CWS according to Table \ref{tbl:close-result}.
But the difference between the neural structure prediction models is not significant.
NN-CRF performs better than the best neural semi-CRF model (SCONCATE) on NER while the both SRNN and SCONCATE outperform NN-CRF on three CWS datasets.
We address this to the fact either the NN-CRF or the neural semi-CRF merely takes input-level information and not sufficiently adopts segment-level information into the models.

A further comparison on inference speed shows that SCONCATE runs 1.7 times faster than SRNN, but slower than the {\sc NN-Labeler} and NN-CRF, which is resulted from the intrinsic difference in time complexity.

\subsection{Comparing Different Segment Embeddings}

Next we study the effect of different segment embeddings.
Using a segmentation model, we can obtain auto-segmented unlabeled data, then learn the segment embeddings.
In this paper, we tried two segmentation models.
One is the neural semi-CRF baseline which represents segment by composing input and another one is the CRF model using sparse hand-crafted features.
For convenience, we use {\sc SEmb-Homo} and {\sc SEmb-Hetero} to note the segment embeddings learned from their auto-segmented data respectively.

\subsubsection{Effect of Pre-trained Segment Embeddings}

\begin{figure}[t]
\centering
\includegraphics[width=\linewidth,trim={0cm 0.4cm 0cm 0.5cm},clip]{learn_curve}
\caption{Negative log-likelihood (blue lines) and development F-score (red lines) by iterations.
Solid lines show the model with randomly initialized segment embeddings.
Dashed lines show that initialized with pre-trained.}
\label{fig:learning-curve}
\end{figure}

We first incorporate randomly initialized segment embeddings into our model and tune the embeddings along with other parameters.
However our preliminary experiments of adding these embeddings into SRNN witness a severe drop of F-score on the CoNLL03 development set (from 92.97\% to 77.5\%).
A further investigation shows that the randomly initialized segment embeddings lead to severe overfitting.
Figure \ref{fig:learning-curve} shows the learning curve in training the NER model.
From this figure, the model with randomly initialized segment embeddings converge to the training data at about 5\textsuperscript{th} iteration and the development performance stops increasing at the same time.
However, by initializing with {\sc SEmb-Homo}, the development set performance increase to 93\%, which shows the necessity of pre-trained segment embeddings.

\subsubsection{Effect of Fine-tuning Segment Embeddings}

\begin{table}[t]
\small
\centering
\setlength{\tabcolsep}{5.5pt}
\begin{tabular}{r||c|ccc}
\hline
\it  model & CoNLL03 & CTB6 & PKU & MSR \\
\hline
  SRNN & 92.97 & 94.56 & 94.86 & 94.80 \\
\hdashline[1pt/3pt]
 \sc +SEmb-Homo w/FT & 92.97 & 95.83 &\bf 96.70 &\bf 97.32 \\
 \sc +SEmb-Homo wo/FT &\bf 93.14 &\bf 95.91 & 96.64 & 96.59 \\
\hline
 SCONCATE & 92.96 & 94.34 & 94.41 & 94.05 \\
\hdashline[1pt/3pt]
 \sc +SEmb-Homo w/FT & 93.07 & 95.79 &\bf 96.75 &\bf 97.29 \\
 \sc +SEmb-Homo wo/FT &\bf 93.36 &\bf 95.88 & 96.50 & 96.44 \\
\hline
 OOV & 46.02 & 5.45 & 5.80 & 2.60 \\
\hline
\end{tabular}
\caption{Effect of fine-tuning (FT) segment embedding on development data.
For CoNLL03 data, a named entity is ``out-of-vocabulary'' when it is not included in the training data as a named entity.}
\label{tbl:tuning-effect}
\end{table}

We study the effect of fine-tuning the segment embeddings by imposing {\sc SEmb-Homo} into our model.
Table \ref{tbl:tuning-effect} shows the experimental results on development data.
We find that our models benefit from fixing the segment embeddings on CoNLL03.
While on MSR, fine-tuning the embeddings helps.
Further study on the out-of-vocabulary rate shows that the OOV rate of MSR is very low, thus fine-tuning on segment embeddings help to learn a better task-specified segment representation.
However, on CoNLL03 data whose OOV rate is high, fine-tuning the segment embedding harms the generalization power of pre-trained segment embeddings.

\subsubsection{Effect of Heterogeneous Segment Embeddings}

\begin{figure}[t]
\includegraphics[width=\linewidth,trim={0cm 0.8cm 0cm 0.5cm},clip]{segrep}
\caption{Comparison between models with {\sc SEmb-Homo} and {\sc SEmb-Hetero} on development data.
The rows show different baseline neural semi-CRF models and the columns show whether fine-tuning (FT) the segment embeddings.}
\label{fig:bl-vs-crf}
\end{figure}

In previous sections, our experiments are mainly carried on the segment embeddings obtained from homogeneous models.
In this section, we use our {\sc Sparse-CRF} as the heterogeneous model to obtain {\sc SEmb-Hetero}.
We compare the models with {\sc SEmb-Hetero} and {\sc SEmb-Homo} on the development data in Figure \ref{fig:bl-vs-crf}.
These results show that {\sc SEmb-Hetero} generally achieve better performance than the {\sc SEmb-Homo}.
On the CoNLL03 and MSR dataset, the differences are significant.
Meanwhile, we see that fine-tuning the segment embedding can narrow the gap between {\sc SEmb-Hetero} and {\sc SEmb-Homo}.

\subsubsection{Final Result}

\begin{table}[t]
\small
\centering
\begin{tabular}{r||c |c c c}
\hline
\it  model & CoNLL03 & CTB6 & PKU & MSR \\
\hline
\sc NN-labeler & 88.62 & 93.06 & 92.99 & 93.79 \\
\sc NN-CRF & 89.08 & 93.65 & 93.28 & 94.17 \\
\hline
\sc Sparse-CRF & 83.43 & 95.08 & 95.06 & 96.54 \\
\hline
 SRNN &88.63 & 94.06 & 93.91 & 95.21 \\
\sc +SEmb-Hetero & 89.59 &\bf 95.48 & 95.60 & 97.39 \\
 &\it +0.96 &\it +1.42 &\it +1.69 &\it +2.18 \\
\hdashline[1pt/3pt]
 SCONCATE & 89.07 & 93.96 & 93.57 & 94.53 \\
\sc +SEmb-Hetero & \bf 89.77 & 95.42 &\bf 95.67 &\bf 97.58 \\
&\it +0.70 &\it +1.43 &\it +2.10 &\it +3.05 \\
\hline
\end{tabular}
\caption{Comparison between baselines and our neural semi-CRF model with segment embeddings.}
\label{tbl:seg-result}
\end{table}

At last, we compare our neural semi-CRF model leveraging additional segment embeddings with those only represent segment by composing input.
Table \ref{tbl:seg-result} shows the result on the NER and CWS test data.
Style of segment embeddings ({\sc Homo} or {\sc Hetero}) and whether fine-tune it is decided by the development data.
From this result, we see that segment-level representation greatly boost up model's performance.
On NER, an improvement of 0.7\% is observed and that improvement on CWS is more than 2.0\% on average.

We compare our neural semi-CRF model leveraging multi-levels segment representation with other state-of-the-art NER and CWS systems.
Table \ref{tbl:ne-stoa} shows the NER comparison results.
\begin{table}[t]
\small
\centering
\begin{tabular}{r|r||c}
\hline
\it genre & \it model & CoNLL03 \\
\hline
\multirow{2}{*}{\it NN} & \cite{Collobert:2011:NLP:1953048.2078186} & 89.59 \\
& \cite{DBLP:journals/corr/HuangXY15} & 90.10 \\
\hdashline[1pt/3pt]
\multirow{3}{*}{\it non-NN} & \cite{Ando:2005:FLP:1046920.1194905} & 89.31 \\
& \cite{guo-EtAl:2014:EMNLP2014} & 88.58 \\
& \cite{passos-kumar-mccallum:2014:W14-16} & \bf 90.90 \\
\hline
\multicolumn{2}{r||}{our best} & 89.77 \\
\hline
\end{tabular}
\caption{Comparison with the state-of-the-art NER systems.}\label{tbl:ne-stoa}
\end{table}
The first block shows the results of neural NER models and the second one shows the non-neural models.
All these work employed hand-crafted features like capitalization.
\textcite{Collobert:2011:NLP:1953048.2078186}, \textcite{guo-EtAl:2014:EMNLP2014}, and \textcite{passos-kumar-mccallum:2014:W14-16} also utilize lexicon as an additional knowledge resource.
Without any hand-crafted features, our model can achieve comparable performance with the models utilizing domain-specific features.

\begin{table}[t]
\small
\centering
\setlength{\tabcolsep}{5.4pt}
\begin{tabular}{r|r||c c c}
\hline
\it genre & \it model & CTB6 & PKU & MSR \\
\hline
\multirow{4}{*}{\it NN} & \cite{zheng-chen-xu:2013:EMNLP} & - & 92.4 & 93.3 \\
& \cite{pei-ge-chang:2014:P14-1} & & 94.0 & 94.9 \\
& \cite{pei-ge-chang:2014:P14-1} w/bigram & - & 95.2 & 97.2 \\
& \cite{DBLP:journals/corr/KongDS15} & & 90.6 & 90.7 \\
\hdashline[1pt/3pt]
\multirow{4}{*}{\it non-NN} & \cite{Tseng05aconditional} & - & 95.0 & 96.4 \\
& \cite{zhang-clark:2007:ACLMain} & - & 95.1 & 97.2 \\
& \cite{sun-EtAl:2009:NAACLHLT09} & - & 95.2 & 97.3 \\
& \cite{wang-EtAl:2011:IJCNLP-2011} &\bf 95.7 & - & - \\
\hline
\multicolumn{2}{r||}{our best} & 95.48 &\bf 95.67 &\bf 97.58 \\
\hline
\end{tabular}
\caption{Comparison with the state-of-the-art CWS systems.}\label{tbl:cws-stoa}
\end{table}

Table \ref{tbl:cws-stoa} shows the comparison with the state-of-the-art CWS systems.
The first block of Table \ref{tbl:cws-stoa} shows the neural CWS models and second block shows the non-neural models.
Our neural semi-CRF model with multi-level segment representation achieves the state-of-the-art performance on PKU and MSR data.
On CTB6 data, our model's performance is also close to \textcite{wang-EtAl:2011:IJCNLP-2011} which uses semi-supervised features extracted auto-segmented unlabeled data.
According to \textcite{pei-ge-chang:2014:P14-1}, significant improvements can be achieved by replacing character embeddings with character-bigram embeddings.
However we didn't employ this trick considering the unification of our model.

\section{Related Work}
Semi-CRF has been successfully used in many NLP tasks like information extraction \cite{NIPS2005_427}, opinion extraction \cite{yang-cardie:2012:EMNLP-CoNLL} and Chinese word segmentation \cite{andrew:2006:EMNLP,sun-EtAl:2009:NAACLHLT09}.
Its combination with neural network is relatively less studied.
To the best of our knowledge, our work is the first one that achieves state-of-the-art performance with neural semi-CRF model.

Domain specific knowledge like capitalization has been proved effective in named entity recognition \cite{ratinov-roth:2009:CoNLL}.
Segment-level abstraction like whether the segment matches a lexicon entry also leads performance improvement \cite{Collobert:2011:NLP:1953048.2078186}.
To keep the simplicity of our model, we didn't employ such features in our NER experiments.
But our model can easily take these features and it is hopeful the NER performance can be further improved.

Utilizing auto-segmented data to enhance Chinese word segmentation has been studied in \textcite{wang-EtAl:2011:IJCNLP-2011}.
However, only statistics features counted on the auto-segmented data was introduced to help to determine segment boundary and the entire segment was not considered in their work.
Our model explicitly uses the entire segment.

\section{Conclusion}

In this paper, we systematically study the problem of representing a segment in neural semi-CRF model.
We propose a concatenation alternative for representing segment by composing input units which is equally accurate but runs faster than SRNN.
We also propose an effective way of incorporating segment embeddings as segment-level representation and it significantly improves the performance.
Experiments on named entity recognition and Chinese word segmentation show that the neural semi-CRF benefits from rich segment representation and achieves state-of-the-art performance.

\section*{Acknowledgments}
This work was supported by the National Key Basic Research Program of China via grant 2014CB340503 and the National Natural Science Foundation of China (NSFC) via grant 61133012 and 61370164.

\bibliographystyle{named}

redpajama_set_name: RedPajamaArXiv


text:
Science Fiction, Space, Wow
14 October 2013 josephshoer 6 Comments
I've been a delinquent spacecraft engineer, and didn't see "Gravity" until today.
In short: it was awesome. It's a tremendous story about courage, fear, perseverance, the human spirit, our ability to solve the most insurmountable problems, and triumph in the face of adversity. It's also visually, sonically, musically, and generally aesthetically breathtaking. The integration of the stunning visuals, physically accurate sound, camera movement through space and spacecraft, and music was extraordinarily well integrated into a complete artistic whole.
And, although the events depicted in the movie would not (or could not) play out exactly as shown, they are all plausible from a physics standpoint.
Everyone should go see it. And, yes, see it in 3D – because this is the first movie I have ever seen in which the 3D adds to the visuals and the drama.
Don't let go.
Before I read any other physicists' reviews, I'm going to go through some of the concepts and sequences in the movie, make a few points about the physics involved, and then explain why I am totally fine about it all.
Spoilers ho!
The debris cascade
As depicted: The Russians conduct a missile test, destroying a satellite. While at first this seems like no big deal, the debris causes a catastrophic chain reaction with other spacecraft – and generates a cloud of hypervelocity missiles that tries to shred our heroes every 90 minutes.
Any problem? Several years ago, the Chinese military conducted a similar test, which caused a big stir in the international space community as other nations worried about the resulting debris cloud. On top of that, some researchers predict that after the amount of space debris reaches a critical level, a cascade will occur where the debris crashes into more debris, creating even more debris, which could demolish any operational satellites in orbit – it's called the Kessler Syndrome.
The debris cloud shown in the movie even bears a striking resemblance to simulations of the debris from the 2009 collision of Iridium 33 and Kosmos 2251, another scary space debris event from real life.
However, there is little chance that the debris from a collision would become catastrophic within minutes of the event. Kessler Syndrome relies on orbit dynamics, and the chain reaction would take many orbits to build up. The debris also would not encounter the Space Station every 90 minutes (once per ISS orbit) except if it's in certain specific orbits, so that particular item is unlikely. (At least, it's unlikely immediately following the cascade.)
Why that's okay: The debris cascade is a real fear in the space community. NASA even conducts "debris avoidance maneuvers" to push manned spacecraft out of the way of potential impacts. The timeline in the movie is accelerated…but this is something we spacecraft engineers worry might happen.
I've got to let go
As depicted: Two astronauts are flung away from the Space Station – caught only by an improvised tether. They strain for a moment at the end of the line, until one astronaut realizes he has to let go to release the tension in the cables. He sacrifices himself to let the other survive.
Any problem? Yes. As depicted, it appears that astronauts Stone and Kowalsky should have behaved exactly as Stone's Soyuz capsule does later in the film: when they reach the end of the tether, it should snap them both back to the station. The only reason for both astronauts to be pulled consistently outward is if there is a force acting on them. This is potentially an error in the physics, and it involves a crucial plot point.
Why it's okay: There are conceivable situations when one astronaut might have to let go of the tether to let the other survive. For example, if the entire system was rotating, centrifugal force would pull both astronauts outward. That's just one obvious example: maybe one of the astronauts instead gets irretrievably stuck or something.
I'm willing to give the director the benefit of the doubt here in favor of getting a deeper human drama.
As depicted: The Kessler Syndrome cascade eliminates all contact between the astronauts and Houston's Johnson Space Center (where Gene Kranz Ed Harris is once again in Mission Control).
Any problem? A Kessler debris cascade would be limited in orbital space – that is, a low Earth orbit cascade would generate debris in low Earth orbit. Communications between NASA spacecraft and mission control are maintained by the TDRS spacecraft, which are geostationary. Those spacecraft will be fine, and so will communications.
Even if the satellite communications system is destroyed entirely, the astronauts still make ground passes over Mission Control. They'll be able to talk to the ground during those passes, just like in the old Apollo days.
Why that's okay: There are more effects that could disrupt communications than destruction of the TDRS system. If the astronauts suffered impacts on their transceivers or antennae, that would certainly do it. It's also possible that the debris cloud simply creates a lot of scattering sources co-orbiting with the astronauts, making the radio link difficult to maintain.
But, in the end, this is a story about being alone and reaching out for human contact. It's about being alone and reaching into oneself to find a reservoir of strength. It's a good story, and the loss of communication is a device to keep it going. Infrequent, garbled communications with the ground might have added to the realism, but would not add very much to the movie as a whole.
Sunday driver
As depicted: Dr. Stone uses one of the International Space Station's docked Soyuz capsules to move to the Chinese space station Tiangong.
Any problem? Well…the ISS, Tiangong, and Hubble Space Telescope are all in different orbits and would not likely be near one another all at once. They would certainly not all be in visual range for the time periods shown in the movie.
Why that's okay: It's not inconceivable to get from ISS to Tiangong in a Soyuz. Both stations are at the same orbit altitude, which means that the Soyuz wouldn't have to add or subtract any energy from its orbit to get from one to the other – so not much delta v is necessarily involved (though I'm not sure whether the Soyuz landing rockets provide enough, and those stations are at different orbital inclinations). [Updated 14 Oct 13: A friend more familiar with Tiangong's orbital parameters than I am tells me that it would take several kilometers per second of delta v…so, no, the Soyuz landing system could not do this.] What it would definitely take is time, especially depending on the orbit phasing between them. Again, the movie compressed the plausible timeline in favor of a more dramatic story; although it stretches the limits of plausibility, the situation is not necessarily impossible. [With a big enough rocket.]
Shaky station
As depicted: The Tiangong space station is losing altitude and entering the atmosphere. Its solar arrays shiver in the wind of the exosphere, and the modules vibrate against one another dramatically from the forces of drag.
Any problem? Not really. Space stations are subject to atmospheric drag, and we typically have to "reboost" them to keep them in space. However, the movie implies – since Tiangong is losing altitude shortly after the ISS was struck – that Tiangong is falling due to the Kessler event. If a debris impact caused any explosions on Tiangong, there could have been enough momentum transfer to knock the station Earthward.
…Except that if the station was vibrating that much, there had to be a significant amount of force on the station. Such forces would only happen deep enough in the atmosphere that the station would already be lost. There would be no time for Dr. Stone to rendezvous and get aboard.
Why that's okay: The vibration is a device used to convey urgency to the viewer. Dr. Stone drops a line about how Tiangong is losing altitude, but there would be no visual or auditory cues to the audience to relate how dangerous the situation is. It's an enhancer, not a critical point.
Coming down
As depicted: The Shenzou spacecraft is tumbling wildly as it enters the upper atmosphere. In a few moments, it straightens out and falls heat-shield-forward until parachutes deploy.
Any problem? Actually – no! One of the advantages of a re-entry capsule design is that it's passively stable when falling heat shield first, meaning that the capsule probably will straighten out. [updated 14 Oct 13] maybe not! My fiancee, who is way the heck more knowledgeable about aerodynamics than I am, informs me that Soyuz capsules are passively stable for small perturbations from their nominal reentry orientation, but not for large perturbations or tumbles. For the capsule to survive, it has to get into the proper orientation using its thrusters. The thruster operation could happen automatically. Whether that happens in time is a good question, though. Certainly, what's shown in the movie is at the very edge of (or probably beyond) the normal operational parameters of the Shenzou vehicle!
All in all, I'd say Dr. Ryan Stone's triumphant attitude when she stands on solid ground is entirely well deserved.
Because, in the end, this is a tremendous movie and a good story. The physics issues are few and far between, and they don't detract from the drama or characters. And there's so much the movie gets right, from its depictions of the Earth, to the behavior of tethers, to the spacecraft hardware, to the masterful use of realistic sound.
Gravity is how you do a space movie.
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6 thoughts on ""Gravity""
Walabio says:
Now, I am even more uncertain about whether I want to watch this movie:
half the reviews state that the physics are so bad that it makes it hard to suspend disbelief. Other reviews state that, although the movie takes dramatic license, as all movies must do, it is still a great movie, with the necessary bending of the physics being acceptable. I suppose that this is 1 of those movies people either love or hate, with no middle ground.
I have yet to see the comment about it being difficult to suspend disbelief due to poor depiction of physics from someone with an obvious technical background. Neil deGrasse Tyson and Phil Plait both seem to share my point of view, and astronauts from different eras have made positive comments. Definitely see it.
I followed your advice and saw Gravity. Its bad physics knocked me out of the movie (the giant invisible spacesquid who pulled away Kowalski was just ridiculous). Still, I hope that this movie brings attention to the problem of spacedebris. I do not want to just complain, so I have something positive for you:
It is Planetes. It is set in the late 21st Century. Kessle-Syndrone has made Near-Earth very dangerous. The solution until the mid21 Century was to try to dodge debris and swallow the cost of damage and replacement. That changed when debris took out a civilian spaceshuttle carrying over 100 people —— ¡it always takes preventable death to get people off of their fat asses!
When someone wants to launch something, one must put down a deposit for the right to the orbit. if one deorbits all of the stages and successfully deorbits the satellite at the end of its lifetime, one gets back the deposit. The unclaimed deposits finance a bounty-system for cleaning Near-Earth Space. The show follows the crew of the ToyBox, a ship which cleanup debris. Maybe, you can write a review.
I agree that that was the most jarring instance of potentially flawed physics; I noted it in my review. Though even I winced at that one in the theaters, I willfully decided to suspend disbelief and I thought the result was worthwhile.
I usually find the bad aesthetics of anime knock me right out of the movies, but I will give Planetes a shot so I can bring some thoughts back to you. 😉
You probable will not like the 3rd episode. Planetes is good, but in that episode, Planetes explores the fact that space is hazardous by the ToyBox capturing debris which turns out to be a coffin with the body of an astronaut who died prematurely from cosmic-ray induced cancer. The thing is that he was launched to Venus which threw him to Jupiter which threw him to Jupiter which ejected him from the solar system. Something went wrong, so he ended up in Near-Earth Space. The probability of that happening is practically 0. Other than that, it is good. Even that episode is otherwise good:
Corporate regulations dictate that all off world employees must write a new will annually. Every day in space, they experience as much radiation as an year on Earth; so now, an year in space exposes them to 4 lifetimes-worth of radiation. Collecting orbital debris is extremely dangerous work which kills many debris-collectors.
The whole show goes in the opposite of glamor:
Their spacesuits are ugly armored monstrosities. The First time we see the chiefdebiscollector, he is half-dressed for space with his diaper visible.
The show goes for gritty realism.
UPDATE: On second viewing, I have decided that the physics in the scene where Kowalski dies are 100% correct.
It's the same thing as Bean's trick with the rope in Ender's Game. The astronauts end up at the end of the tether/parachute cord, and because their incoming velocity was not coaligned with the tether's attachment point to the ISS, the tether swings them in an arc about its attachment point. Centrifugal force is pulling them outwards, and Kowalski realizes that the cord won't hold that load but he can give Stone relief by letting go – at which point he flies straight out from the station.
Also on second viewing, I noticed that the camera yanks around all over the place – until the scene where Stone has her hallucination and starts to get some serious control over her situation. Then she might still be zipping all over, but the camera is rock-steady.
This movie is so freaking awesome.

redpajama_set_name: RedPajamaCommonCrawl


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We offer a wide range of customized services including transportation services with a driver for the day, private guided walking tours in the Barrio Gotico, and specialized wine tours to several wineries with wine tasting. We do have limited resources however, and if you are traveling in the peak season we recommend that you contact us as soon as possible as we do get fully booked on many days for these services.
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text:
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Provider: Xfinity
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You can catch him in various Clubs around town in the weekends or every Tuesday here and exclusively at Pulzar FM.


Tamworth's Entertainment Centre has been taken over by arts and crafts, as the Craft Alive expo returns for another year.
But there's a familiar face in Armidale's Jackson Cook – the 16-year-old teaching at the expo for the first time.
Previous articleCAN WOLLONGBAR-ALSTONVILLE HOLD ON TO THEIR UNION CHAMPIONSHIP FOR A FIFTH YEAR?


text:
The Communist Party of Lesotho (Mokhatio oa Makomonisi a Lesotho) is a communist party in Lesotho, founded on May 5, 1962. The founding secretary of the party was John Motloheloa. Other early leaders of the party included Mokhafisi Kena and Sefali Malefane. Edward Mofutsanyana was a member of the Communist Party of South Africa Central Committee (disbanded 1950) but had no part in the Lesotho party. Joe Matthews of the South African Communist Party (founded 1953) financed the launch of the Lesotho party but later supported the ANC-allied Marematlou Freedom Party.  The South African Communist Party, the Hungarian Socialist Workers Party and Walter Ulbricht of the Socialist Unity Party of Germany sent greetings on the occasion of the founding of the party. The party began publishing Mosebetsi.

During the 1960s, the party split into Soviet-allied (Kena, Malefane, Mtji, and Ms Hoohlo) and Chinese-allied (Motloholoa, T. Leanya, and T. Nqojane) factions. In the 1970 election, its two Moscow-allied candidates at Thaba Chitja (Kena) and Tsoelike (Malefane) received 25 and 18 votes respectively. The party was officially banned in February 1970, but went underground and continued its activities. After the 1986 coup, its leader, Sefali Malefane, a university lecturer in Economics, was made a minister in Major-General Justin Lekhanya's regime. The party was made legal again in 1991.

After the fall of apartheid rule in neighbouring South Africa, the party advocated union with South Africa.

The party ceased the publication of its newspaper, Mafube, in 1997.

The party's current leader is Manny Stevenson.

References

1962 establishments in Basutoland
Lesotho
Political parties established in 1962
Political parties in Lesotho
Socialism in Lesotho

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During the late Cretaceous, volcanic eruptions covered what's now the Western Ghats in India.
Several species went extinct before the impact 66 million years ago, adding to evidence that volcanoes were a culprit in the mass die-off.
Whereas most geological evidence about the mass extinction has been gathered from the North American continent, Laiming Zhang at China University of Geosciences in Beijing and his colleagues looked toward northern China. The region, they argue, is far enough from both the impact crater in Mexico and the ancient lava flows known as the Deccan Traps in India, that it's an ideal location to test for far-ranging climatic signals related to both events.
Although that jives with previous data showing pre-impact temperature swings across the world, the team also made a more alarming find. Zhang and his colleagues discovered that many fossils within the ancient sediments disappeared at the time of this warming. Two-thirds of the extinctions within the region actually occurred after the onset of volcanism and before the impact.
As such, the study provides proof that the volcanoes destabilized the climate, priming the world for catastrophe, argues co-author Paul Wignall, a paleontologist at the University of Leeds.
"Our paper swings the pendulum back a little more toward volcanism," he says.
While he admits that the new research does suggest there was some early ecological distress that coincided with the Deccan Traps volcanism, he would like to see evidence of this trend in places other than China.
"There isn't anything about that single basin that equals a global phenomenon," he says.


Background Reports or Background Checks that are inaccurate can cost you your job. These reports are often governed by the Fair Credit Reporting Act as a consumer report. An employer must disclose or tell you in writing that it will obtain such a report and it must get your permission in writing and in advance of obtaining such a report. Your permission must be requested in a single document, not combined with an application. If the employer takes some adverse action, like firing you from your job or not hiring you, based on information in the background report, then it must also inform you of this fact in advance of taking the action, give you a copy of the report and an opportunity to correct any inaccuracies. When an employer jumps the gun and fires or fails to hire without the advance notice and opportunity to correct, the employer is likely in violation of your rights under federal law. Such violations carry the right to recover damages.


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5 Min Science Experiment
"The scale of the experiments is remarkable," says Michael Littman. The team with the most captures after 5 minutes wins. Players also fire a laser to tag enemies, sending them back to their home.
What Does Dtf Do In Organic Chemistry two different groups on one end of the bond and two different groups on the other end. An alkene such as but-2-ene has two different groups on each alkene carbon. It can exist as #E-Z# isomers that differ in the positions of the substituents on the double-bonded atoms. In organic chemistry lingo an r stands
11 qiuck science experiments can be done in just 5 minutes! Learn hands-on about chemical reactions, density, and more.
Here are five easy DIY biology experiments that you can do at home to have fun with biology without the need for. Clara Rodríguez Fernández – 13/08/2018 5 mins – Tops. Here are. The main goal is to have fun with science and get curious.
Apr 24, 2017 · Science experiments don't require a lot of special equipment or a long time to set up. You can perform fun and educational experiments right in your own home that are just as exciting and intriguing as those you'd conduct in a laboratory, and you can do them in only 10 minutes.
Houghton Mifflin Math Homework Book Grade 5 Every student, from kindergarten to 12th grade, has access to at least one technological device. The district also uses educational programs, such as Houghton Mifflin Harcourt's GO Math. grade book. 1st Grade ABCya Activities · 1st Grade CCSS Math Websites – Spotlight on. Houghton-Mifflin Math Kids: 5th Grade · Internet 4 Classrooms 5th Grade Sites♥
Who has time to scour the Internet searching for science experiment ideas? I know I don't! Save your time and sanity with Five Minute Science!. This fun science book includes 21 science experiments for kids you can do in five minutes or less!
Homework made easy: 20 easy science experiments to avoid last minute school assignment panic. September 15, 2015. If you think science experiments are all about Bunsen burners and beakers, think again. Here are 20 of the best science experiments for kids using things from your pantry and recycling bin: no goggles required.
Repeat the science experiment with cool water, veggie oil, milk or any other liquid and record what caused the candy to dissolve the fastest, which took the longest. Things to Do > Carefree Crafting > 39 Classic Science Experiments for Kids. 17 Photo Hacks to Capture Every Minute.
5 Minute Science Experiments for Kids on Frugal Coupon Living. Geometric Bubbles, Glow in the Dark Volcanoes, Replicate Ocean Currents and more!
Easy Science Experiments for Kids. Kids love to explore the world around them – but isn't it disappointing when you set up a science experiment for a home school lesson and it doesn't work? The 31 easy science experiments listed here are all fun to try – with equipment you will easily find at home. They are simple and quick – but give great.
Take a break for a few minutes outside "Cairo'," one of our kitchens in San Francisco. The coffee is hot, and the view is fabulous. In online experimentation platforms, we choose the experiments.
Aiming to dispel the myths and make with the science, I enlisted two dedicated cannabis. Everclear on the cannabis flowers while stirring with a spoon. 5. Let flowers sit for 10 to 15 minutes.
WHY NOT TRY A FUN SCIENCE EXPERIMENT RIGHT NOW? Here's list of great science experiments with instructions that you can do right at home or at school. In order for your science experiment to be safe and successful, be sure to: Get your parent's or teacher's permission, and their help; Follow the directions as written
In late April, the residents of Aguas Zarcas in Costa Rica saw a giant fireball light up the sky as it hurtled towards the ground and broke up into hundreds of pieces in…
There is not a single doubt in my mind that everyone swears, whether it's a teenager trying to look cool for their friends or an adult who took the wrong exit and added another 20 minutes to their.
Nov 24, 2016 · See more of 5-Minute Crafts on Facebook. Log In. Forgot account? or. Create New Account. Not Now. 5-Minute Crafts. November 24, 2016 · 4 stunning science experiments.
Health Science Center Tulsa Tech May 12, 2018. This is the spring 2018 Commencement Ceremony for the Oklahoma State University Center for Health Sciences College of Osteopathic. Nov 30, 2018. Accrediting Bureau of Health Education Schools. Date: November. American Medical Sciences Center. Glendale. CA. Ross Medical Education Center. American Institute of Medical Technology. Tulsa. OK. Caris College. Houghton Mifflin Math
Dec 4, 2017. Got 5 Minutes? Make kids smart by eating marshmallows! Well, maybe not these marshmallows. But this science experiment help kids learn.
But you can actually do some pretty mind-blowing, hands-on science experiments at home using stuff you probably have lying around the house. Don't worry if you never made it past biology: These 50 science activities are super-easy and a lot of fun to boot, as kids are exposed to a wide variety of scientific concepts.
Jun 26, 2014. This fun DIY science experiment give you the full step-by-step. Make Your Own Lava Lamp: A very simple 5-minute science experiment.
The blood pressure cuff was inflated for 5 minutes and then deflated for 5 minutes. the presence of a condition in the body — compared with their levels before the experiment. Specifically, they.
Since 1933, ham radio operators across North America have established temporary ham radio stations in public locations during Field Day to showcase the science and skill of amateur. on June 22 from.
Apr 02, 2017 · 5 Minute Science Experiments for Kids. Pin to Pinterest. It doesn't matter how many years that I have been out of the classroom, I still have a heart to instruct and learn. With this comes doing sciences experiments with my children.
Money Flow 5.8 Enrich Go Math "Many young people graduate without a basic understanding of money and money management, business, the economy, and investing. We hope to help educators, home schoolers, parents, and guardians teach children these skills, while reinforcing basic math… What kinds of curricula and maths enrichment? Should kids attend after-school classes. At one point, I was overseeing the
NCERT Solutions for Class 7 Science, Chapter 9 – Soil is. Razia conducted an experiment in the field related to the rate.
Apr 11, 2015. 12 Fun Science Experiments for Your Little Mad Scientist. 5. Empathy-Building Design Exercise: Building a project for another child (or group.
[Smile Secrets: 5 Things Your. molars for 5 minutes, and then brushed the teeth gently. She repeated this procedure three times daily for 10 days, much like the recommendations given by those.
What Is Microorganism In Biology Methanogens are a group of archaea that produce methane gas as a part of their metabolism. They are anaerobic microorganisms that use carbon dioxide and hydrogen to produce energy. Methane is produced as a byproduct. Methanogens are anaerobic archaea and are poisoned by oxygen. Organization. Multicellular organisms—such as humans—are made up of many cells. The
Since its harrowing touchdown on Mars a year ago in Gale crater on August 5, 2012, PDT (August 6. "Working on a Mars rover is very different from doing science experiments in the lab on Earth," he.
Science experiments + a cute and furry blue monster that we know and love = YES. Hosts Annie Colbert and Matt Silverman have clearly helped discover the.
Find lots of easy Science Experiments perfect for trying out home or at school!
To achieve this, all it took was 16 minutes at 425 degrees Fahrenheit (230 degrees Celsius), plus a short 5-minute preheat. You can use an oven thermometer to get exact temperatures or experiment.
Of those who report their rapes, around 4–5% also describe experiencing orgasm. in reality you wanted to be raped and you're. loving every minute of it. that fact alone makes 'rape' an act of.
Olmsted was exaggerating; his claim was based less on science than on. when they sat through 15 minutes of nature scenes and birdsong in a 3-D virtual reality room than when they sat in a plain.
May 09, 2017 · It's a science lesson, an experiment, and an observation journal in one! Science Experiments You Can Do In 15 Minutes. 1. Gather a few supplies and let the kids learn how to make an egg float! 2. This seed jar experiment does need a few days to complete, but it only takes a few minutes to set up! And the kids won't mind watching their seeds.
Like most great science fiction. inspired by the time-consuming demands of their interactive experiment 'Bandersnatch', which was originally intended as the fourth episode in Season 5. However,
Belen Masia, assistant professor of computer science at the University of Zaragoza in Spain It is extremely important to find what you need in the scientific literature. Do I spend more time on my.
When teaching a science class, it can be difficult to find time to include exciting science activities. Luckily, there are a few science activities that are easy and can be completed in as little as 5 minutes. Using short science activities like these will enable you to.
like Europe's Ariane 5 and the upcoming Ariane 6. Sounding rockets, like this Rexus (Rocket Experiments for University Students) being assembled at the ZARM facilities in Bremen, Germany, are launched.
Oct 21, 2016 · See more of 5-Minute Crafts on Facebook. Log In. or. Create New Account. See more of 5-Minute Crafts on Facebook. Log In. Forgot account? or. Create New Account. Not Now. 5-Minute Crafts Videos Science experiments. English (US) · Español · Português (Brasil) · Français (France) ·.
"It's hard to track them even for a 5-minute video," Crall. the researchers report today in Science. The team discovered that the lethargy was, inexplicably, stronger at night. In a further.
In an initial observational study, researchers found that many coffee shop visitors sitting in pairs or in small groups checked their phones every 3 to 5 minutes. designed a follow-up experiment to.
Here are 10 wonder-filled, hands-on science experiments for kids — to illuminate God's powerful presence in. Watch for a few minutes to see what the raisins do. Leave it for 5 to 10 minutes, then place several ice cubes on top of the lid.
Experiments over the decades — some intentional. a nuclear engineer and radiation safety specialist at Rensselaer Polytechnic Institute, 5 and 6 Sieverts (Sv) over the course of a few minutes will.
Experiment using the scientific method. I"ll provide a one-minute warning. After the 5 minute limit, the teams may look at each other"s work, comparing the.
The Uplift biorhythmic acupressure system guides you to resync your body clock in 3 easy steps that takes about 5 minutes when you arrive. based on the combination of the latest neurological.


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Yala National Park - Sri Lanka
Yala National Park is in southeastern Sri Lanka and borders the Indian Ocean. It consists of 378 square miles divided into five blocks, only two of which are open to the public. Block I is 54.44 square miles and was named a national park in 1938. Block II, 38.34 square miles, was added in 1953; Block III, 157.32 square miles, was added in 1967; Block IV, 102 square miles, was added in 1969; and Block V, 25.7 square miles, was added in 1973. It has some adjacent parks to it as well, including Kumana NP (or Yala East NP), Yala Strict Nature Reserve, Kataragama, Katagamuw and Nimalawa sanctuaries are contiguous with Yala. Block I is the main tourist area, but Block III and Yala East are gaining in popularity. We visited in March 2019 with Sanjay of Lankatracker and highly recommend his services. We spent two mornings and an afternoon in Yala, one morning and afternoon in Block III, which has fewer tourists, and a morning in Block I.
Map from the official website.
Map from Wikipedia
Elephant rock is in the background.
Judy on Yala beach, on the Indian Ocean, with Patanangala, a rock outcrop, in the background. This is one of the few places we could get out of the vehicle.
Our safari vehicle, at a lunch break.
Yala has the highest concentrations of leopards in the world, as well as elephants and sloth bears which are also attractions. I saw three leopards, we saw elephants and Sanjay saw a sloth bear, but we couldn't see it before it disappeared.
Following are photos from our visit:
Sri Lankan axis deer
Sri Lankan sambar deer
Sri Lankan leopard. Seen the first day.
We saw two leopards the second day, together on a large rock.
Wild water buffalo
Sri Lankan elephant. We saw elephants in Yala, but we saw more of them in other national parks.
Stripe-necked mongoose. The first morning Sanjay said we were only looking for leopards the first two hours and were not stopping for other animals. He stopped for this one. I'm assuming it was not a common animal to see.
Tufted gray langur. Seen near the entrance to Yala.
Indian wild boar
Indian hare
Grizzled giant squirrel
Mugger crocodile
Bengal monitor lizard
This Bengal monitor lizard was next to our hotel just outside Yala NP.
Indian flapshell turtle.
Gray-headed swamphen
Lesser whistling duck
Crested hawk eagle
Crested serpent eagle
Common kestrel
Black-headed ibis
Jungle fowl
White-breasted kingfisher
Peafowl
Yellow-wattled lapwing
Asian open-billed stork
Judy May 24, 2021 at 4:35 PM
This is a park with a lot of different thins to enjoy. Besides the incredible wildlife and the beautiful scenery, I was really impacted by seeing the beach area where the massive Indian Ocean tsunami hit in 2004 and had horrific consequences. In spite of the very bumpy and dusty dirt road, this was one of my favorite drives on the trip.
Forster's Tern
Lake Nakuru National Park - Kenya
Shaba National Reserve - Kenya
Buffalo Springs National Reserve - Kenya
Mount Kenya National Park - Kenya
Polonnaruwa - Sri Lanka
Kandy Lake - Sri Lanka
Udawalawe National Park - Sri Lanka
Western Burrowing Owl - Owlets
Bundala National Park - Sri Lanka
Wetlands Between McDonald and Hazard Roads near Ni...
Varner Harbor at Salton Sea State Recreation Area
Western Burrowing Owl (May 8, 2021)
Wilson's Phalarope
Red-Necked Phalarope
Wilson's Warbler
Horned Lark
Willet (Western)
Western Tanager
Yellow-Headed Blackbird
Hudsonian Whimbrel


There is no subject I've touched upon here at Steak and a Bible that has gotten more pushback than cessationism. It seems whenever I bring up the subject that I get a string of comments, including personal attacks that I consider unfit for publication because they try to judge my relationship with Jesus. In argumentation, ad hominem attacks are the weakest form of argument. With the goal of trying to elevate this discussion above such personal criticisms and straw men caricatures of either side I share this article with you all.
The Cripplegate posted this piece from Nathan Busenitz entitled: What Cessationism is Not.
Busenitz goes on to explain four things that cessationism is not: 1) Cessationism is not anti-supernatural, nor does it deny the possibility for miracles. 2) Cessationism is not founded on one's interpretation of "the perfect" 1 Corinthians 13:10. 3) Cessationism is not an attack on the Person and work of the Holy Spirit. 4) Cessationism is not a product of the Enlightenment.
I found this explanation very helpful, although I admit the second point was a bit out of my depth and I'm going to need to reread it. I urge you, whether you are a cessationist or a continuationist, to check out this clarifying article from Busenitz at The Cripplegate and let's leave the straw men behind.
This entry was posted in Christian Life, The Word and tagged Bible, Cessationism, Cripplegate, Holy Spirit, Jesus, Nathan Busenitz. Bookmark the permalink.
Cessationism is the only reasonable choice.
How are we to "test" anything except through God's Written Word?
We cannot rely on experiential, feeling-based testing. That is capriciousness, at best. Also, we cannot rely on our thought life as it, too, is faulty (stinkin' thinkin').
I believe cessationism has somehow become synonymous with Deism when that is simply not the case.


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Written By: Philip Pullman
Read By: David Harewood
The Adventures of That Most Enterprising Feline
0 Hours and 22 Minutes Imprint: Listening Library Ages 5-7 years Release Date: December 15, 2020
Imagine how Jacques feels when he learns that his older brothers have inherited the mill and the donkey, and all he's got is the CAT!
Well, he's not fed up for long, because he soon discovers that Puss is no ordinary cat... Not only can he talk (which is pretty unusual for a cat), but he's also got brains. And he's got a plan - a plan that involves Jacques, a Royal Princess and a very large fortune!
However, there are some things that Puss hasn't included in his plan, such as a very hungry Ogre, some sleepless Ghouls and more than a touch of magic . . .
Artwork illustrations © Ian Beck, 2000
Cover and text design @ Puffin
Published by arrangement with Random House Children's Publishers UK, a division of The Random House Group Limited.
Juvenile Fiction - Animals - CatsJuvenile Fiction - Fairy Tales & Folklore - AdaptationsJuvenile Fiction - Royalty
PP Philip Pullman
DH David Harewood


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Teen (obsolete)
Fangirl Freakout
We need to talk about Spider-Man
From Our Teen Readers
With the new Spider-Man movies creating new buzz in the superhero universe, a very important question has been raised: Who will be the next Spider-Man? Of course, we'll all miss Andrew Garfield, but it's exciting to think of what a new leading man will bring to the story. For months, several names have been thrown around, like Asa Butterfield—star of Hugo and Ender's Game—and Tom Holland from Billy Elliot the Musical. Today, we got confirmation that Holland will be the lucky actor suiting up as Spidey next.
I have no doubt that Holland will be perfectly adequate as Spidey, but I also can't help but think he's a sort of, well, boring choice. How many times do producers want us to sit through the same stories over and over again with the same type of hero we're supposed to be rooting for? The Spider-Man series needs something different to keep the fandom interested. The Internet has been whirling with disappointment over the noticeable white-washing of superheroes over the past few years, and casting a Spider-Man that defies that trend would have really sent a message to the fans of the Marvel universe. There are plenty of actors of color that would be perfect to take on the challenge and sling some webs around NYC. So, even though I'm happy for Tom Holland, here are some actors I would have loved to have seen as Spidey in the next movie.
Donald Glover of Community has a pretty big following among Spider-Man's diversity-seeking fanbase, and it's easy to see why. His comedic timing is ideal to deliver Spidey's snarky lines. He has already loaned his voice to Ultimate Spider-Man: Web-Warriors as Miles Morales, so he has superhero experience under his belt.
Stephan James
Stephan James, hot off of his success in Selma, is another great candidate for the job. He's young, talented, and a fresh face to the world of action. We could expect a more serious Spider-Man with James behind the mask, but with a smolder like that, his performance would be welcomed with open arms.
Alfred "Alfie" Enoch
And then there's Alfred "Alfie" Enoch from How to Get Away with Murder. Besides being British—which obviously gives him a leg up on the hotness meter—Alfie's killer charm and wicked acting skills would make for a Spidey that'd make us all swoon. Just look at that face! He's even got his hand ready to shoot some webs. Oh, and he played Dean Thomas in the Harry Potter series. NBD.
Dayo Okeniyi
Another worthy contender is Dayo Okeniyi, better known as Thresh from The Hunger Games series. Okeniyi has experience with the various stunts needed to master Spider-Man's signature moves. The Hunger Games kept him busy in the action department and let him showcase his many combat skills on the battlefield. Okeniyi would promise a truly action-packed set of movies that are sure to thrill.
It's all fun and games to wish for a Spidey audiences have never seen before, but it's something else for that wish to become a reality. Marvel needs to stand up for what the fans want to see, and a carbon-copy of Andrew Garfield is not the lead many of us were hoping for. Another Marvel comic franchise has taken a step in the right direction. The Fantastic Four, set to release August 2015, includes Michael B. Jordan on its cast list. Jordan will play Johnny Storm a.k.a. the Human Torch, a role that was previously played by Chris Evans. Why not add some diversity to another, even more popular, franchise?
Since Marvel has decided to cast Holland, I fear we're not going to add anything to the Spider-Man story this time around, and that we might be headed toward yet another reboot of the same story we've already seen told by Tobey Maguire and Andrew Garfield before. Since the Spider-Man franchise seems to get a reboot every few years, here's hoping that with the next round, Marvel will cast a person of color as Spidey.
Rumor has it Spidey is going to be introduced in the newest Captain America movie, Captain America: Civil War, when it is released in 2016, while the new Spider-Man franchise is set to kick off in 2017.
Jordan is currently a college student trying to learn as much as she can. She can reference Harry Potter like no other, and is always down for chocolate chip waffles. You can follow her Tumblr if you want to know more about what her mind looks like.
(Images via here, here, here, here, and here.)
By From Our Teen Readers
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All Topics in Fangirl Freakout
Hello Giggles Teen


Macrame arch at The Edwards Bar for 'The Impossible Market'.
The super sized macrame arch is 11 foot wide x 7 foot high.
'Mermania' wall hanging collaboration with Tim Neve.
Collaboration 'Mermania' wall hanging for the official photoshoot for renowned Interior Stylist Tim Neves launch of wallpaper, cushions and tiles. Miriam's work was also on display at Tim Neves stand at the 2015 Newcastle home show.


In her book The Conscious Entrepreneur, author and coach Laura C. Cannon teaches you how to harness your unique gifts and the power of your own consciousness to maximize your satisfaction - and deepen your enjoyment - of your business and your life.
Grounded in research from the fields of modern psychology, eastern philosophy, and personal development, this book explains six core principles necessary for radical, transformational change - personally and professionally. By teaching you to use your business as the vehicle for your personal growth, The Conscious Entrepreneur delivers timeless wisdom and practical exercises to help you think, act, and live the path to your highest potential - without adding to your already-packed schedule. Forget work-life balance, get ready to shift into work-life integration for more happiness, stronger relationships, and a higher return on your investment in every sense of the word.
You have the power to dream wildly and to create the life you really want. You have the power to change EVERYTHING – your career, yourself, and even the world – simply by becoming more conscious.
Powerful transformation comes from a radical shift in perspective. This simple truth is the cornerstone of my work.
In order to have more joy, peace, and fulfillment in our lives we must raise our conscious awareness and begin to see our lives and our work in a new way.
A member of the prestigious National Speaker's Association with more than 15 years of experience as a professional public speaker, Laura has a knack for delivering engaging and informative presentations. She's spoken to groups ranging from 12 to 5000 on a wide range of topics, from office productivity to motivation and empowerment to mindfulness and meditation. She has been a guest speaker for colleges, civic organizations, businesses, and associations. Laura holds a B.S. from Towson University and an M.A. in Human Sciences (Psychology) from Hood College with a specialization in Thanatology and Grief Counseling. Laura is also a 200hr RYT yoga and meditation instructor.


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text:
import React from 'react';
import { storiesOf, action } from '@kadira/storybook';
import Pagination from '../index';
import '../styles/Pagination.styl';

storiesOf('Button', module)
  .add('Simple example', () => (
    <Pagination
      totalResults={100}
      resultsPerPage={10}
      urlPattern="search"
      onChangePage= {action('Change page')}
    />
  ))
  .add('Navigate on click', () => (

      <Pagination
        totalResults={100}
        resultsPerPage={10}
        urlPattern="search"
        onChangePage= {action('Change page')}
        preventNavigate={false}
      />

        *If you click in a page, then you have to refresh the page

  ))
  .add('Show begining and end', () => (
    <Pagination
      totalResults={100}
      resultsPerPage={10}
      urlPattern="search"
      onChangePage= {action('Change page')}
      showBeginingEnd
    />
  ))
  .add('Show next and previous', () => (
    <Pagination
      totalResults={100}
      resultsPerPage={10}
      urlPattern="search"
      onChangePage= {action('Change page')}
      showPreviousNext
    />
  ))
  .add('Change initialPage', () => (
    <Pagination
      totalResults={100}
      resultsPerPage={10}
      urlPattern="search"
      onChangePage={action('Change page')}
      initialPage={3}
    />
  ))
  .add('Change from outside the actual page', () => (
    <TestActualPage />
  ))
  .add('Send a custom class to component', () => (
    <Pagination
      totalResults={100}
      resultsPerPage={10}
      urlPattern="search"
      onChangePage={action('Change page')}
      className="CustomPagination"
    />
  ))
;

class TestActualPage extends React.Component {
  constructor(props) {
    super(props);
    this.state = {
      actualPage: 1,
    };
    this.handleChangePage = this.handleChangePage.bind(this);
  }

  handleChangePage(e) {
    this.setState({
      actualPage: parseInt(e.target.value, 10),
    });
  }

  render() {
    return (

        Actual Page:
        <input type="text" onChange={this.handleChangePage} />
        <Pagination
          totalResults={100}
          resultsPerPage={10}
          urlPattern="search"
          onChangePage={action('Change page')}
          actualPage={this.state.actualPage}
        />

    );
  }
}

redpajama_set_name: RedPajamaGithub


BRASILIA – The former speaker of Brazil's lower house of Congress was convicted on Thursday of corruption and sentenced to 15 years and four months in prison.
Eduardo Cunha, who was the driving force behind the legislative effort that led to former President Dilma Rousseff's ouster from office last year for violating budget laws, has been jailed since October, three months after he was forced out of office.
Judge Sergio Moro accepted the arguments of prosecutors and found Cunha guilty of receiving bribes from a crude-production contract that state oil company Petrobras signed in the African nation of Benin, as well as of money laundering and tax evasion.
"There can be no offense more serious than that of someone who betrays his legislative mandate and the sacred trust deposited in him by the people for his own benefit," Moro said.
Cunha received $1.5 million in bribes for facilitating the signing of the contract, but the financial hit to Petrobras was considerably higher, according to the sentence, which is subject to appeal.
The conservative evangelical is the highest-profile political figure to be convicted in the so-called Lava Jato (Car Wash) probe, which has led to the arrest and conviction of dozens of politicians, Petrobras officials and executives at leading Brazilian construction and engineering companies.
The investigation has centered on allegations that Odebrecht and other large engineering companies paid bribes to corrupt Petrobras officials in exchange for inflated contracts and funneled money to politicians who provided cover for the graft.
Prosecutors also have requested that the Supreme Court investigate roughly 100 politicians who enjoy immunity from prosecution in ordinary courts, including some of current President Michel Temer's Cabinet ministers, according to documents obtained by the local press.
In Brazil, high-ranking politicians such as federal lawmakers and Cabinet ministers can only be tried by the Supreme Court, which has come under criticism for its slow pace in taking up and resolving cases.


The very name of the slot is reminiscent of China and all things Chinese – traditional houses, ancient vases, Chinese beauties and dragons looking more like green dogs.
The slot has five reels and twenty-five paylines. It is interesting how RTG, the famous manufacturer and the releaser of Golden Lotus, has decided to determine the sum for the jackpot: here, it is exactly 8888 coins.
The second prize is less impressive, though: 1250 coins. The values for those coins are for some reason excessively (?) numerous: $0.01, $0.02, $0.03, $0.04, $0.05, $0.06, $0.07, $0.08, $0.09, $0.1, $0.25, $0.5, $1, $2.5, and $5. This is the usual way RTG decides upon the coins' value, so there is no need to wonder.
You can bet one coin per payline, gambler, and hope for one of the 43 winning combinations to occur. Both wilds and scatters are in the game, supplementing your winning combinations and giving you up to 20 free spins. Auto spinning is available, if anything.
Golden Lotus is the slot worth your attention. Verdict: recommended.