text:
\section{Introduction}

Biological pathways encode sequences of biological reactions, such as phosphorylation, activation etc, involving various biological species, such as genes, proteins  \cite{aldridge2006physicochemical, kitano2002systems}. Studying and analyzing pathways is crucial to understanding biological systems and for the development of effective disease treatments and drugs \cite{creixell2015pathway, khatri2012ten}. There have been numerous efforts to reconstruct detailed process-based and disease level pathway maps such as Parkinson disease map (Fujita et al., 2014), Alzheimers disease Map \cite{mizuno2012alzpathway}, mTOR pathway Map \cite{caron2010comprehensive}, and the TLR pathway map \cite{oda2006comprehensive}. Traditionally, these maps are constructed and curated by expert pathway curators who manually read numerous biomedical documents, comprehend and assimilate the knowledge in them and construct the pathway.

With increasing number of scientific publications manual pathway curation is becoming more and more impossible. Therefore, Automated Pathway Curation (APC) and semi-automated biological knowledge extraction has been an active research area \cite{ananiadou2010event,ohta2013overview,szostak2015construction} trying to overcome the limitations of manual curation using various techniques from hand-crafted NLP systems \cite{allen2015complex} to machine learning techniques \cite{bjorne2011extracting}.  Machine-learning NLP systems, in particular, show good performance in BioNLP tasks, but they are still performing less good in automated pathway curation, partly because there have been few attempts to measure the performance of NLP systems for APC directly.

Recently, there has been some attempt at remedying the situation and new datasets and evaluation measures have been proposed. For instance, Spranger et al. \shortcite{spranger2016measuring} use the popular human-generated mTOR pathway map \cite{caron2010comprehensive,efeyan2010mtor,katiyar2009redd1} and quantify the performance of a particular APC system and its ability to recreate the complete pathway automatically. Results reported were mixed.

One of the key components in such APC systems is identification of triggers, events and their relationships. These machine learning-based systems are essentially just supervised classification components.

This paper explores whether we can improve results of automated pathway curation for mTOR pathway by using different training datasets and learning algorithms. We show that the choice of event extraction classifiers increases F-score by up to 20\% compared to state-of-the-art system. Our results also show that within limits the choice of training data has significantly less impact on results than the choice of classifier. Our results also suggest that additional research is necessary to solve the problem of APC.

\section{Automatic Pathway Curation}
\label{s:automatic-pathway-extraction}
We constructed an automatic pathway curation system that take as input scientific articles in PDF format and transforms them into SBML encoded, annotated pathway maps. The pipeline has multiple steps.
\begin{enumerate}
\setlength\itemsep{-0.2em}
\item PDFs are translated into pure text files using the cermine\footnote{http://cermine.ceon.pl/index.html} tool.
\item Preprocessing provides tokenization, POS tagging, dependency and syntax parsing.
\item An \emph{event extraction system} extracts the mentions of entities (genes, proteins etc), reactions (e.g. phosphorylation) and their arguments (theme, cause, product).
\item A converter constructs pathways from the information provided by the event extraction system.
\item An annotation system maps extracted entities and events to Entrez gene identifiers and SBO terms.
\end{enumerate}

The following sections detail steps 3 to 5.

\subsection{Event Extraction}
\label{s:event-extraction}
We used the TURKU Event Extraction System (TEES) for event extraction \citep{bjorne2010complex}. This system is one of the most successful BioNLP systems. It has not only won 1st place in BioNLP competitions but was also the only one NLP system that participated in all BioNLP-ST 2013 tasks \cite{bjorne2012university}. The system combines various NLP techniques to extract information from text. TEES workflow consists of four steps:

\begin{enumerate}
\setlength\itemsep{-0.2em}
\item Trigger Detection - detection of named entities and event triggers in a given sentence to construct nodes of the event graph.
\item Edge detection - construction of complex events linking few triggers to create event graph. Output produced during this step is often a directed, typed edge connecting two entity nodes.
\item Unmerging - event nodes from merged event graph are duplicated in order to separate arguments into valid combinations. This step is needed for evaluation of final results in BioNLP Shared Task standard.
\item Modifiers detection - final component that defines additional attributes for events such as speculation and negation modifiers.
\end{enumerate}

By default TEES trains a different instance of multiclass Support Vector Machines (SVM) for each step. Recent versions of TEES \cite{bjorne2015tees} allow to easily exchange the SVM classifiers with other supervised classification algorithms. For example, all \textit{scikit-learn} multiclass, supervised learning algorithms that support sparse feature matrices can be applied \citep{scikit-learn}. Thanks to this it is possible to  test different algorithms for event extraction task and automatic pathway extraction. For this paper, we exchanges classifiers in all steps 1-4s as described in Section \ref{par:classifiers}. The output of TEES is a standoff formatted representation of entities and events.

\subsection{Conversion Standoff to SBML pathways}
In principle events and entities extracted by TEES correspond to biological species and reactions. We translate the NLP representation into SBML -- the standard, XML-based markup language for representing biological models \cite{hucka2003systems}. SBML essentially encodes models using biological players called {\tt sbml:species}\footnote{We refer to SBML vocabulary using the prefix ``sbml".}. {\tt sbml:species} can participate in interactions, called {\tt sbml:reaction}.  Species participate in interaction as {\tt sbml:reactant}, {\tt sbml:product} and {\tt sbml:modifier}. The basic idea being that some quantity of reactant is consumed to produce a product. Reactions are influenced by modifiers. The mapping algorithm is adopted from and described in more detail in Spranger et al. \shortcite{spranger2015extracting}.

\subsection{SBO/GO, Entrez Gene Annotations}
The SBML encoded, automatically extracted pathway is further \emph{annotated} using Systems Biology Ontology (SBO) \citep{le2006model} and Gene Ontology (GO) terms. SBO also provides a class hierarchy for reaction types. For instance, the NLP system identify phosphorylation reactions, which are a subclass of conversion reactions. All reactions in the data are automatically annotated with SBO/GO term (coverage 100\%) using an annotation scheme detailed in \citep{spranger2015extracting}.

Species (e.g. proteins, genes) were annotated using the gene/protein named entity recognition and normalization software GNAT  \citep{hakenberg2011gnat} - a publicly available gene/protein normalization tool. GNAT returns a set of Entrez Gene identifiers \citep{maglott2005entrez} for each input string. Species were annotated using all returned Entrez Gene identifiers for a particular species (organism human).
We call the set of Entrez Gene identifiers returned by GNAT for each species \emph{Entrez Gene signature}.

\section{Classifiers for Event Extraction} \label{par:classifiers}
In this paper we evaluate classifiers for event extraction (Section \ref{s:event-extraction}) and their impact on the overall performance of the automatic pathway extraction system. We compare the  following classifiers:

\begin{itemize}
\setlength\itemsep{0em}
\item \textbf{Support Vector Machines (SVM)} is the default TEES classifier \citep{joachims1999making}. It was optimized for linear classification and its performance scales linearly with the number of training examples.
\item \textbf{Decision Tree (DT)}  creates a model that can predict the target value by learning simple decision rules inferred from the training data. Compared to the other techniques they are relatively fast, cost of using tree is logarithmic in the number of examples. We use Gini impurity criterion to evaluate quality of the split.
\item \textbf{Random Forest (RF)} classifiers fit a number of ensembled decision tree classifiers, each built from a bootstrap sample of a training set. The best split of node is chosen only from a random subset of the features, not all features. Final classifiers are combined by averaging their probabilistic prediction. Single tree have a higher bias but, due to averaging variance of the random forest as a whole decreases.
\item \textbf{Multinomial Naive Bayes (MNNB)} This is an implementation of the naive Bayes algorithm for multinomial data which is one of the classic variants used in classification of discrete features (e.g. text classification). Additive smoothing parameter was set to 1.
\item \textbf{Multi-layer Perceptron (MLP)} MLP is a feedforward neural network model. We use hidden layer with 100 neurons and rectified linear unit activation function. We optimize for logarithmic loss  using stochastic gradient descent. Learning rate is constant and equal to 0.001.
\end{itemize}

For DT, RF, MNNB and MLP we use implementations from \textit{scikit-learn} Python library \citep{scikit-learn}.

\begin{table}[th!]
\begin{center}
\begin{tabular}{l|lll}
Item          & ANN      & GE11  & PC13  \\ \hline
Documents     & 60       & 908   & 260   \\
Words         & 11960    & 205729 & 53811 \\
Entities      & 1921     & 11625 & 7855  \\
Events        & 1284     & 10310 & 5992  \\
Modifiers     & 71       & 1382  & 317   \\
Renaming      & 101      & 571   & 455
\end{tabular}
\caption{\label{tab:corpora}Corpora statistics}
\begin{tabular}{lrrr}
\hline
{Reaction type}		& 			ANN 		&	  GE11		&  PC13 \\
\hline
Acetylation         &                     0  &         0  &        38  \\
Activation          &                     0  &         0  &       359  \\
Binding             &                   211  &       988  &       606  \\
Catalysis           &                    87  &         0  &         0  \\
Conversion          &                     0  &         0  &       124  \\
Deacetylation       &                     0  &         0  &         1  \\
Degradation         &                     0  &         0  &        49  \\
Demethylation       &                     0  &         0  &         4  \\
Dephosphorylation   &                    14  &         0  &        22  \\
Deubiquitination    &                     0  &         0  &         3  \\
Dissociation        &                    55  &         0  &        54  \\
Gene\_expression     &                    46  &      2265  &       384  \\
Hydroxylation       &                     0  &         0  &         1  \\
Inactivation        &                     0  &         0  &        76  \\
Localization        &                    27  &       281  &        96  \\
Methylation         &                     0  &         0  &         7  \\
Negative\_regulation &                   194  &      1309  &       801  \\
Pathway             &                     0  &         0  &       443  \\
Phosphorylation     &                   252  &       192  &       406  \\
Positive\_regulation &                   235  &      3385  &      1506  \\
Protein\_catabolism  &                    18  &       110  &         0  \\
Regulation          &                   132  &      1113  &       707  \\
Transcription       &                     8  &       667  &        74  \\
Translation         &                     1  &         0  &        11  \\
Transport           &                     0  &         0  &       189  \\
Ubiquitination      &                     4 &         0 &        31 \\
\hline
\end{tabular}
\caption{\label{tab:corpora_reactions}Reaction types annotated for training data sets.}
\end{center}
\end{table}

\section{Datasets}
\subsection{Training Datasets}
In order to quantify the impact of training data, we test the following three training sets.

\begin{itemize}
\setlength\itemsep{0em}
\item \textbf{ANN} - consists of 60 abstracts of scientific papers from Pubmed database related to the mTORpathway map. This dataset was human-annotated for NLP system training \cite[Corpus annotations (c) GENIA Project]{ohta2011pathways} .
\item \textbf{GE11} consists of 908 abstracts and full texts of scientific papers used in BioNLP ST 2011 GENIA Event Extraction task as training data \cite{kim2012genia}.
\item \textbf{PC13} consists of 260 abstracts of scientific papers used in BioNLP ST 2013 Pathway Curation task as training data \cite{ohta2013overview}. The task goal was to evaluate the applicability of event extraction systems to support the automatic curation and evaluation of biomolecular pathway models.
\end{itemize}

The overall corpora statistics are summarized in Table \ref{tab:corpora}. GE11 and PC13 have the largest number of annotated events. ANN is much smaller in comparison. Also, the distribution of event types differs between data sets (Table \ref{tab:corpora_reactions}). GE11 uses more general terms (Binding, Regulation) compared to PC13 where some specific events appear only a few times (Deacetylation, Hydroxylation, Methylation).

We train classifiers on four combinations of the three training datasets: 1) standalone GE11; 2) GE11+ANN - combined GE11 and ANN; 3) combined GE11+PC13+ANN - GE11, PC13 and ANN; 4) PC13+ANN - combined PC13 and ANN. For instance, DT+GE11 refers to a decision tree classifier trained on GE11.

We use GE11-Devel BioNLP ST2011 dataset for hyperparameter optimization of all classifiers.

\subsection{Test Data}
Performance of classifiers is tested on the mTOR pathway map \cite{caron2010comprehensive}. The map was constructed by expert human curators using 522 full text papers from the PubMed database. The experts curated a single large map using CellDesigner \cite{funahashi2008celldesigner} - a software for modeling and executing mechanistic models of pathways. CellDesigner represents information using a heavily customized XML-based SBML format  \cite{hucka2003systems}.

{\bf Target Human expert data} We translate the curator map into standard SBML and further enrich the information using SBO/GO and Entrez Gene annotations. For SBO/GO, we use existing annotations provided by curators and extend them by automatic annotations deduced from reactants and products of reactions. For example,  if a phosphoryl group is added in a reaction, it is annotated using the SBO term for phosphorylation. Each reaction may be annotated with multiple SBO/GO terms. Also we annotate the curated map with Entrez gene identifiers (similar to the automatic extraction data). We call this pathway \emph{TARGET}.

{\bf Testing classifiers} The 522 full text papers -- used by human curators for the construction of the mTOR pathway -- are used for evaluating the different text mining classifiers. For this, we plug in (trained) classifiers into the automatic pathway extraction pipeline which performs preprocessing, event extraction, conversion to SBML and annotation (see  also Section \ref{s:automatic-pathway-extraction}). The output of this is an annotated SBML file that is subsequently compared to human-curated SBML-encoded pathway data.

\section{Evaluation}
Evaluation of the classifiers (and the system as a whole) is performed by comparing the automatically extracted pathway with the hand-curated pathway. Spranger et al. \shortcite{spranger2016measuring} propose a number of graph overlap algorithms for quantifying the difference and similarity of two pathways. Here we employ the same measures. The following summarizes the strategies.

{\bf Species} In order to decide whether species in two pathways are the same, we use the name of the identifiers and their Entrez gene signatures.
\begin{description}\setlength\itemsep{-0.5em}
\item[nmeq: ] Two species are equal if their names are exactly equal. We remove certain prefixes from the names (e.g. phosphorylated).
\item[appeq: ] Two species are equal if their names are approximately equal. Two names are approximately equal iff their Levenshtein-based string distance is above 90 \citep{levenshtein1966binary}
\item[enteq: ] Two species are equal if their entrez gene identifiers are exactly equal. This basically translates to the two species bqbiol:is identifier sets being exactly the same (order does not matter).
\item[entov: ] Two species are equal if their entrez gene identifiers sets overlap. This basically translates to the two species bqbiol:is identifier sets overlapping.
\item[wc: ] Human curated data contains complex species that contain other species as constituents (species that consist of various proteins etc). \emph{wc} allows species to match with constituents of complexes.
\end{description}

{\bf Reaction} match based on their SBO/GO annotations
\begin{description}
\setlength\itemsep{-0.5em}
\item[sboeq: ] Two reactions are equal iff their signatures are exactly the same. That is, the whole set of SBO/GO terms of one reaction is the same as of the other reaction.
\item[sboov: ] Two reactions are equal, iff their signatures overlap. That is, the intersection of the set of SBO/GO terms of one reaction is with the set of SBO/GO terms of the other reaction is not empty.
\item[sobisa: ] Two reactions are equal, iff there is at least one SBO/GO term in each signature that relate in a is\_a relationship in the SBO reaction type hierarchy. For instance, if there is a phosphorylation reaction and a conversion reaction, then \emph{sboisa} will match because phosphorylation is a subclass of conversion according to the SBO type hierarchy.
\end{description}

{\bf Edges} only match if their labels are strictly equal. So if an edge is a reactant, then it has to be a reactant in the other pathway. Same holds for products and modifiers.

{\bf Subgraph matching strategies} are combinations of matching strategies for species, reactions (and for edges which is always the same). For instance, the matching strategy \emph{nmeq, sboeq} is the most strict and requires that species names are exactly equal and that SBO/GO signatures of reactions are exactly equal. The matching strategy \emph{appeq/enteq/wc, sboisa} is the most loose strategy. In this strategy, two species match if their names are approximately equal or if their Entrez gene identifiers overlap or if any of this applies to one of the constituents of the two species. Two reactions match if any of their SBO/GO terms are in a \emph{is\_a} relationship. We compare a total of 24 matching strategies.

{\bf Subgraph overlap} is computed as follows. For each subgraph in the extracted pathway we search for subgraphs in the human curated data that match according to some subgraph matching strategy.  We use \emph{micro-averaged F-score}, precision and recall \citep{sokolova2009systematic} for quantifying the retrieval results. F-score is used to quantify the overlap of species, reactions and edges. We then macro-average these results to get a \emph{total F-score} quantifying performance of the extraction system as a whole.

\section{Results}
Some classifiers take long to train, so we only have partial results for MLP. However, all other classifiers (DT, MNNB, RF, SVM) finished training on all selected combinations of training data sets.

Since we tested 24 subgraph overlap measures with 18 classifiers, we receive a lot of data that cannot be discussed in detail in this paper. Here, we concentrate on general trends in the data. Code and datasets are published as appropriate\footnote{\url{https://github.com/sbnlp/2017BioNLPEvaluation/}}.

\begin{table*}
\tiny
\centering
\begin{tabular}{lrrrrrrrrr}
\hline
 name               &  \multicolumn{1}{l}{\rot{\# species}} &  \multicolumn{1}{l}{\rot{ \# reactions}} &  \multicolumn{1}{l}{\rot{ \# compartments}} &   \multicolumn{1}{l}{\rot{\# edges }}&   \multicolumn{1}{l}{\rot{\# reactant edges}} &   \multicolumn{1}{l}{\rot{\# edges product}} &   \multicolumn{1}{l}{\rot{\# modifiers}} &   \multicolumn{1}{l}{\rot{\# isolated species}} &   \multicolumn{1}{l}{\rot{\# isolated subgraphs}} \\
\hline
 DT+GE11            &      282361 &         92899 &              201 &    195531 &              89001 &             91895 &              14635 &             118162 &                 187871 \\
 DT+GE11+ANN        &      284187 &         95096 &              188 &    212490 &             100529 &             93886 &              18075 &             115427 &                 184542 \\
 DT+GE11+PC13+ANN   &      289504 &         94496 &              208 &    207447 &              94044 &             93559 &              19844 &             118281 &                 188013 \\
 DT+PC13+ANN        &      279647 &         82977 &               20 &    188325 &              86802 &             82469 &              19054 &             123309 &                 184698 \\
 MLP+GE11+ANN       &      278510 &         88502 &              230 &    193150 &              88655 &             87636 &              16859 &             114541 &                 182456 \\
 MNNB+GE11          &      264413 &         69744 &              202 &    137828 &              61448 &             69250 &               7130 &             139402 &                 198972 \\
 MNNB+GE11+ANN      &      245680 &         45690 &                0 &     86771 &              40102 &             45676 &                993 &             166712 &                 206606 \\
 MNNB+GE11+PC13+ANN &      269008 &         68926 &                0 &    142712 &              70292 &             68894 &               3526 &             151495 &                 203903 \\
 MNNB+PC13+ANN      &      287314 &         76932 &                0 &    183029 &              94693 &             76925 &              11411 &             154210 &                 199844 \\
 RF+GE11            &      227613 &         29573 &                9 &     50444 &              20786 &             29133 &                525 &             178233 &                 206874 \\
 RF+GE11+ANN        &      261414 &         67974 &              347 &    130556 &              57195 &             67271 &               6090 &             136180 &                 199157 \\
 RF+GE11+PC13+ANN   &      203314 &         32075 &                1 &     58083 &              25312 &             31704 &               1067 &             146342 &                 177371 \\
 RF+PC13+ANN        &      236220 &         37018 &                0 &     68559 &              30493 &             36909 &               1157 &             168927 &                 204771 \\
 SVM+GE11           &      288421 &         98938 &              451 &    200595 &              89769 &             97791 &              13035 &             109060 &                 191175 \\
 SVM+GE11+ANN       &      262327 &         81207 &              388 &    169841 &              73033 &             80203 &              16605 &             109862 &                 177023 \\
 SVM+GE11+PC13+ANN  &      275303 &         85435 &              312 &    179661 &              77587 &             84549 &              17525 &             114941 &                 184481 \\
 SVM+PC13+ANN       &      275256 &         82119 &               59 &    177651 &              79239 &             81512 &              16900 &             120729 &                 186122 \\
 TARGET             &        2242 &           777 &                7 &      2457 &               1044 &               892 &                521 &      15 &                 4 \\
\hline
\end{tabular}
\caption{General statistics of all datasets. Number of extracted species, reactions and compartments. Total number of edges and of product, reactant and modifier edges. The table also shows the number of  isolated species and the number of unconnected subgraphs for each pathway. The human curated mTOR pathway \emph{TARGET} numbers are shown in the last row.}
\label{t:general-statistics-dataset}
\end{table*}
\subsection{Extraction Results: Species, Reactions, Subgraphs}
Generally speaking the extracted pathways contain two order of magnitudes more species reactions, and edges than the \emph{TARGET} pathway (see Table \ref{t:general-statistics-dataset} for all results). This is normal since the extracted pathways consist of all combinations of entity and event mentions in text. The same entities may occur more often in the text then they are referenced in the actual pathway.

Our results show that extraction classifiers perform inconsistent with respect to the identification of compartments. While some classifiers retrieve a lot of compartment information (via localization events), others (especially MNNB trained on ANN and PC13 datasets) do not extract any compartments. MNNB with our parameter choice might not be able to learn many different event types so it skips least frequent reaction types (one of which is localization event).

Measuring how many subgraphs there are per pathway, we can see that more than half of all species extracted by classifiers are isolated and not connected to any reactions. Similarly we see many (small) subgraphs being extracted by the classifiers, whereas \emph{TARGET} consists of essentially one large connected graph (with a few modeling mistakes).

\begin{table}[ht!]
\begin{tabular}{lrr}
\hline
                        &  this & Spr16 \\
 &  f-score & f-score \\
\hline
 nmeq, sboeq            &          {\bf 11.7} &                    7.6 \\
 nmeq, sboov            &          {\bf 15.3} &                   11.4 \\
 nmeq, sboisa           &          {\bf 18.1} &                   13.6 \\
 appeq, sboeq           &          {\bf 12.5} &                    8.1 \\
 appeq, sboov           &          {\bf 16.3} &                   12.0 \\
 appeq, sboisa          &          {\bf 19.4} &                   14.5 \\
 appeq/enteq, sboeq     &          {\bf 16.9} &                   11.9 \\
 appeq/enteq, sboov     &          {\bf 21.7} &                   17.1 \\
 appeq/enteq, sboisa    &          {\bf 26.0} &                   20.4 \\
 appeq/entov, sboeq     &          {\bf 36.2} &                   26.9 \\
 appeq/entov, sboov     &          {\bf 41.9} &                   34.7 \\
 appeq/entov, sboisa    &          {\bf 48.6} &                   39.5 \\
 nmeq/wc, sboeq         &          {\bf 23.3} &                   15.0 \\
 nmeq/wc, sboov         &          {\bf 26.0} &                   19.6 \\
 nmeq/wc, sboisa        &          {\bf 29.1} &                   22.0 \\
 appeq/wc, sboeq        &          {\bf 24.6} &                   15.7 \\
 appeq/wc, sboov        &          {\bf 27.4} &                   20.4 \\
 appeq/wc, sboisa       &          {\bf 30.9} &                   23.1 \\
 appeq/enteq/wc, sboeq  &          {\bf 39.7} &                   29.1 \\
 appeq/enteq/wc, sboov  &          {\bf 45.3} &                   37.2 \\
 appeq/enteq/wc, sboisa &          {\bf 52.0} &                   42.2 \\
 appeq/entov/wc, sboeq  &          {\bf 39.7} &                   29.1 \\
 appeq/entov/wc, sboov  &          {\bf 45.3} &                   37.2 \\
 appeq/entov/wc, sboisa &          {\bf 52.0} &                   42.2 \\
 \hline
 \end{tabular}
\caption{This table compares macro F-score performance of the classifiers discussed in this paper with results reported in Spranger et al. \shortcite{spranger2016measuring}}
\label{f:comparison-last-year}
\end{table}

\subsection{General Trends Subgraphs overlap}

\begin{figure}[ht!]
\centering
\includegraphics[width=1\columnwidth]{subgraphs-overlap-results-best-macro-precision-macro-recall-macro-f-score-per-node-match}
\caption{Best performing classifier f-score, precision and recall for each subgraph overlap functions. The x-axis are the different subgraph overlap function. The y-axis shows precision, recall, f-score of the highest classifier for each subgraph overlap function. Notice that these can be different classifiers for each subgraph overlap function (see Table \ref{t:macro-f-score-results} for all results).}
\label{f:best-node-match-performance}
\end{figure}

Let us first concentrate on overall performance especially with respect to previous results. For this we compute the best classifiers and their score for different matching strategies. For each matching strategy, we evaluate all classifiers and then choose the best performing one and compare it with the results reported in Spranger et al. \shortcite{spranger2016measuring}/Spr16. Table \ref{f:comparison-last-year} shows that the best classifiers outperform Spr16 in all cases and for some subgraph overlap measures by 10 points.

If we analyze the classifiers from this paper in more detail, results (Figure \ref{f:best-node-match-performance}, Table \ref{t:macro-f-score-results}) show that for the strictest matching strategy (\emph{nmeq, sboeq}) the best classifiers reach a macro F-score of 12 (with 14 precision, 13 recall scores). For the loosest strategy (\emph{appeq/entov/wc, sboisa}) this goes up to F-score 52 (47 precision, 66 recall). These results show that when it comes to \emph{exact} extraction the classifiers fail badly, whereas with more looser overlap strategies, performance becomes reasonable and there is some overlap between the extracted and the human-curated data. Of course, this also entails that the automatically extracted pathway does not completely capture what humans are constructing from the text.

Generally speaking overlap strategies that are loose with respect to constituents of complex species (\emph{wc}) outperform their non \emph{wc} counterparts. For instance, \emph{nmeq/wc, sboeq} performs much better than \emph{nmeq, sboeq}. This shows that complex species are important for the mTOR pathway but their extraction is not very detailed - which is why the overlap matching strategy has to be lenient with respect to complex species constituents. The increase in F-score for \emph{wc} matching strategies is primarily driven by an increase in recall score. For instance, the difference between \emph{nmeq, sboeq} and \emph{nmeq/wc, sboeq} is more than 20 points, whereas precision does not improve that much. The reasons for that is that the same subgraphs in the extracted pathway overlap with more subgraphs in \emph{TARGET}. So it is not the case that other subgraphs in the extracted pathway overlap with \emph{TARGET}.

Results also show that recall is in general much higher than precision for looser strategies. For instance, \emph{wc} strategies (right hand side of Figure \ref{f:best-node-match-performance}) double the recall score w.r.t to their precision scores. This also shows that in principle loosening matching strategies impacts mostly recall as the same subgraphs in the extracted data overlap with the human curated data.

\begin{figure}[ht!]
\centering
\includegraphics[width=1\columnwidth]{subgraphs-overlap-results-best-classifiers-histogram-macro-precision}
\includegraphics[width=1\columnwidth]{subgraphs-overlap-results-best-classifiers-histogram-macro-recall}
\includegraphics[width=1\columnwidth]{subgraphs-overlap-results-best-classifiers-histogram-macro-f-score}
\caption{Histogram of best classifiers. This histogram is generated by counting how often a classifier is the best for a particular subgraph matching strategy.}
\label{f:best-classifiers}
\end{figure}

\subsection{Classifier Performance in Detail}
The bottom figure in Figure \ref{f:best-classifiers} shows the best classifiers in terms of precision, recall and F-score. We measured how often a classifier is the best classifier (for each of the 24 subgraph overlap strategies). It is clear that overall Random Forest classifier (RF) performance is the best. For all 24 matching strategies it is a Random Forest classifier that is better than any other competitor with RF trained on PC13 and ANN being the most frequent best classifier overall. Second place is Random Forest trained simply on GE11 (the largest dataset in terms of entities and events). No other classifiers (SVM, MLP, MNNB, DT) outperform RF. Training on all datasets (RF+GE11+PC13+ANN) does not seem to increase success significantly. Performance across different RF classifiers is on par and good (see Table \ref{t:macro-f-score-results})

Results in the top figure of Figure \ref{f:best-classifiers} show that RF has the best precision performance. RF+PC13+ANN is the most frequent best classifier w.r.t precision. RF+GE11 and RF+GE11+PC13+ANN also performing comparably. Compared to recall this means that RF wins F-score because they are best in precision.

No RF classifier performs best in recall. Results show that MLP, DT and SVM all perform well for certain subgraph overlap strategies with SVM being most often the best classifier, followed by various DT-based classifiers and MLP.

\begin{figure}
\centering
\includegraphics[width=.835\columnwidth]{subgraphs-overlap-results-box-plot-macro}
\caption{Statistics of classifier performance across all matching strategies. X-axis - classifiers. Y-axis - macro precision top, macro recall middle and macro f-score bottom (with 100 being perfect score).}
\label{f:box-performance}
\end{figure}

Figure \ref{f:box-performance} gives results for all classifiers across all matching strategies. Looser strategies give the max and strict matching strategies the min data points. We can see that performance is primarily driven by the choice of classifier as the F-score mostly varies with the type of classifier used (even though there are a few outliers). Situation is a bit more varied for precision and recall. Interestingly choice of dataset seems to have less impact. Generally speaking MNNB are the least successful. RF clearly dominate precision on average but are close enough to DT and SVM on recall.

\section{Conclusion}
This paper continues the current trend of extending NLP systems for APC and building more complete systems that allow evaluation with respect to some external standard - here the hand curated mTOR pathway.

We measured the impact of different classifiers on retrieval performance and showed that certain classifiers have the potential to increase retrieval performance. Especially Random Forest classifiers perform much better on mTOR than previously tried Support Vector Machines. On the other hand, the training data choice seems to have little impact (at least for the tested ANN, GE11 and PC13 training datasets).

Spranger et al. \shortcite{spranger2016measuring} argue that not all of the problems of APC can be overcome by using more training data on event extraction systems. They argue that additions such as complex species recognition, co-reference resolution and pathway construction are needed to ultimately solve the problem posed by APC. This certainly remains true and is not directly questioned by results in this paper. The system described here does not automatically compose single pathway maps from the extracted data. Nevertheless, our results suggest that a lot of progress can be made by improving on the event extraction part of the pipeline.

This paper focuses on evaluating current machine learning techniques for event extraction. We are currently in the process of evaluating other systems including rule-based ones.

\section*{Acknowledgments}
We would like to thank the authors of the Turku Event Extraction System for providing such an excellent system to the community. We also thank SBI for making the mTOR dataset available and their collaboration on evaluation and measurements.

redpajama_set_name: RedPajamaArXiv


text:
Q: SPOnline - Follow a site programmatically on behalf of another user I have a site in SharePoint Online that I would like all users in my organization to follow.  I have been trying to use CSOM, Powershell and Rest to achieve that with no success - I can only do that for the current user.
Ideally, I would like the process to run every night and set all new users and users who "un-followed" my site to follow it again. Is it possible?

A: It's not possible as per this documentation:
Common programming tasks for following content in SharePoint
To quote:

Create an instance of a manager object in the context of a specified
  user

*

*CSOM: not implemented

*JSOM: not implemented

*REST: not implemented

*SSOM:  SPSocialFollowingManager (overloaded)

You can only follow for the current user using client-side calls. Unfortunately only SSOM allows managing following for other users.
(With Groups and the Microsoft Graph you are also out of luck as the docs for addFavorite explicitly states it's for the current user only.)

redpajama_set_name: RedPajamaStackExchange


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Our products and systems give you the chance to really understand your information and your data, to interact with it and to foster communication.
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Contact Us page is very essential for every website to capture the leads and get the queries of your customers or audience. A well-designed and responsive contact form help to make your communication with the website visitors easy, effective and efficient. Before contact form comes into the picture, website owners used to put email id on their websites. This, in turn, would often bring lots of spam and also attack the systems with a virus. Joomla Contact Form extensions keep you away from spammers and help to get responses from genuine users.
There are many contact forms builder extensions available in the market, but features and techniques of creating contact forms are different. Today we are demonstrating 5 Best Joomla Contact Form extensions of recent times.
JD Simple Contact form is a totally beginner friendly Joomla contact form extension which can help you to design simple yet lightweight Joomla contact form. This extension is our in-house development and we are using this extension in our templates as well.
Most of the extensions come with lots of features which makes them too heavy but most of the time these features are useless for you. So we decided to develop a really simple contact form with important field types such as text field, text area, radio buttons, checkboxes etc. which are mostly used in the contact forms.
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text:
Q: Static Method on Enum I have been following declaration-merging for typescript and I copied their example for merging a enum with some functions, and all I did was add export to the enum and now I get errors.
export enum MyEnum {
  Point = 'Point',
  MultiPoint = 'MultiPoint',
}

namespace MyEnum {
  export function parse(val: string): Type {
    return MyEnum[val as keyof typeof MyEnum];
  }
}

[ts] Individual declarations in merged declaration 'MyEnum' must be all exported or all local. [2395]
so fair enough it wants all or nothing, so I export out the namespace too.
export namespace Type { ... }

I now get another error.
[ts]
Type 'MyEnum | ((val: string) => MyEnum)' is not assignable to type 'MyEnum'.
  Type '(val: string) => MyEnum' is not assignable to type 'MyEnum'. [2322]
I am not really sure if I am understanding that error message correctly, but it looks like it is trying to say that the MyEnum type can be either be a value of MyEnum or function is obviously failing, and also not what I was trying to do.
I also tried to remove the export from function parse but then it just becomes unavailable. I have tried adding 'static' in various places but nothing seems to work.
All I am trying to do currently is
const value: string = getString();
const pointType = MyEnum.parse(value);

But I do plan on having more advanced stuff in the future and would just like to understand what I am doing wrong?

A: In adding an extra member to the enum (you are adding parse method) you changed the what indexing into the enum will produce and what the keys of enum will be.
keyof typeof MyEnum is going to be "Point" | "MultiPoint" | "parse", so it will include the extra member.
Also typeof MyEnum[keyof typeof MyEnum] will be MyEnum | ((val: string) => MyEnum), including the new member signature.
You can check for parse (which you may want to handle explicitly) :
export enum MyEnum {
    Point = 'Point',
    MultiPoint = 'MultiPoint',
}

export namespace MyEnum {
    export function parse(val: string): MyEnum {
        const key = val as keyof typeof MyEnum
        if (key === 'parse') throw new Error("parse !");
        return MyEnum[key]
    }
}

Or you can just use an assertion that excludes parse:
export enum MyEnum {
    Point = 'Point',
    MultiPoint = 'MultiPoint',
}

export namespace MyEnum {
    export function parse(val: string): MyEnum {
        return MyEnum[val as Exclude<keyof typeof MyEnum, 'parse'>]
    }
}


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text:
Q: Align the content to the center using Tkinter I am trying tkinter for the first time. I want to align all the input box and the contents to the middle of the dialogue box.Currently, it is in the left side. I want the whole thing in the image to be at the center from top and bottom as well.
Here is the image of the output I am currently getting.
.
if __name__ == "__main__":
    _root = Tk()

    style = ttk.Style()
    style.configure("TButton", foreground="black", background="seashell3")
    _root.title('Image Compression using K-means Clustering')
    _root.resizable(width=TRUE, height=TRUE)

    _mainframe = ttk.Frame(_root, padding='5 5 5 5')
    _mainframe.grid(row=0, column=0, sticky=(E, W, N, S))

    _url_frame = ttk.LabelFrame(_mainframe, text='URL', padding='5 5 5 5')
    _url_frame.grid(row=0, column=0, sticky=(E, W),columnspan=2)
    _url_frame.columnconfigure(0, weight=1)
    _url_frame.rowconfigure(0, weight=1)

    _url = StringVar()
    _url.set('http://')
    _url_entry = ttk.Entry(_url_frame, width=100, textvariable=_url)
    _url_entry.grid(row=0, column=0, sticky=(E, W, S, N), padx=5)
    _fetch_btn = ttk.Button(_url_frame, text='Fetch info from URL', command=fetch_url)

    _fetch_btn.grid(row=0, column=1, sticky=W, padx=5)

    _img_frame = ttk.LabelFrame(_mainframe, text='Content', padding='9 0 0 0')
    _img_frame.grid(row=1, column=0, sticky=(N, S, E, W),columnspan=2)
    _images = StringVar()
    _img_listbox = Listbox(_img_frame, listvariable=_images, height=6, width=100, selectmode='multiple')
    _img_listbox.grid(row=0, column=0, sticky=(E, W), pady=5)
    _img_listbox.bind('<<ListboxSelect>>', fetch_selected_images)

    _scrollbar = ttk.Scrollbar(_img_frame, orient=VERTICAL, command=_img_listbox.yview)
    _scrollbar.grid(row=0, column=1, sticky=(S, N), pady=6)
    _img_listbox.configure(yscrollcommand=_scrollbar.set)

    _radio_frame = ttk.Frame(_img_frame)
    _radio_frame.grid(row=0, column=2, sticky=(N, S, W, E))

    _choice_lbl = ttk.Label(_radio_frame, text='')
    _choice_lbl.grid(row=0, column=0, padx=5, pady=5)
    _save_method = StringVar()
    _save_method.set('img')

    _img_only_radio = ttk.Radiobutton(_radio_frame, text='As Images', variable=_save_method, value='img')
    _img_only_radio.grid(row=1, column=0, padx=5, pady=2, sticky=W)
    _img_only_radio.configure(state='normal')

    _scrape_btn = ttk.Button(_mainframe, text='Scrape!', command=save,style='TButton')
    _scrape_btn.grid(row=2, column=0, sticky=(N,E), pady=2)

    _compress_btn = ttk.Button(_mainframe, text='Compress!', command=compress)
    _compress_btn.grid(row=2, column=1, sticky=W, pady=2)

    _status_frame = ttk.Frame(_root, relief='sunken', padding='2 2 2 2')
    _status_frame.grid(row=1, column=0, sticky=(E, W, S))
    _status_msg = StringVar()
    _status_msg.set('Type a URL to start scraping...')
    _status = ttk.Label(_status_frame, textvariable=_status_msg, anchor=W)
    _status.grid(row=0, column=0, sticky=(E, W))

    # context menu

    _menubar = Menu(_root)
    filemenu = Menu(_menubar, tearoff=0)
    filemenu.add_command(label='Fetch images from URL', command=fetch_url)
    filemenu.add_command(label='Scrape images', command=save)
    filemenu.add_command(label="Compress image", command=compress)
    filemenu.add_separator()

A: After you create the root window you must tell it how to react to size changes:
_root.columnconfigure(0, weight=1)
_root.rowconfigure(0, weight=1)

which means to expand row 0 and column 0 with the window.
Then to center the _mainframe just put it in the window without any sticky:
_mainframe.grid(row=0, column=0)


Facelet debug Tag is useful in displaying the component tree and scoped variables.
This tag is useful in displaying the component tree and scoped variables. This information will be displayed in a popup window of browser when we press Ctrl+Shift+(a key). This key will be specified in hotkey attribute. For example, in the code below in "debugtemplate.xhtml", this has been specified "p". So when page comes to the user then if Ctrl+Shift+p is pressed, debug window is open which displays the component tree and scoped variables.
hotkey : This attribute is used to specify the key which is to be pressed with Ctrl+Shift to open a debug window. Its default value is "d". It doesn't take EL expression.


text:
Sport Manchester United
Sam Wallace: 'Ed Woodward now the real king of Old Trafford - and the Glazers are happy to keep it that way'
Woodward: Delivered Mourinho news. Photo: Action Images via Reuters
Sam Wallace
December 19 2018 2:30 AM
As the dust settles on the third Manchester United managerial sacking of the post-Alex Ferguson era, the usual questions are asked about the man at the centre of the club, who arguably has more power at Old Trafford than even the great Scotsman did in his heyday.
https://www.independent.ie/sport/soccer/premier-league/manchester-united/sam-wallace-ed-woodward-now-the-real-king-of-old-trafford-and-the-glazers-are-happy-to-keep-it-that-way-37639937.html
https://www.independent.ie/sport/soccer/premier-league/article37639936.ece/0910e/AUTOCROP/h342/2018-12-19_spo_46645732_I1.JPG
It is Ed Woodward's club now, and the notion of the 47-year-old former investment banker being under any pressure from his bosses in Florida is as remote as United's chances of winning the Premier League this season. Those American venture capitalists who have seen their investment in the highest revenue-generating club in the world more than double in value since 2005 trust Woodward implicitly, and as long as they own United it will be his to run.
It was Woodward who delivered the news to Jose Mourinho face-to-face at the club's training ground yesterday morning, and it would have been Woodward who reached out to Ole Gunnar Solskjaer and Mike Phelan to steady the ship in the aftermath.
Not a natural hire-and-fire executive, Woodward has not enjoyed those awkward conversations over the years with David Moyes and Louis van Gaal, but accepts them as part of the job.
He first came to the Glazers' attention working for JP Morgan in the 2005 leveraged takeover, and moved to United to revolutionise the way in which top clubs approached commercial deals, breaking up their market according to territories and industry sector partnerships.
In the most recent Deloitte Football Money League, United retained their place at the top with an annual revenue of £581.2 million, with 48 per cent of that flowing from the commercial partnerships that Woodward had put in place.
As a businessman capable of reinventing the modern football goliath as a cash machine, Woodward is unparalleled, but it will be his appointment of Mourinho's successor that, outside of Florida, the club's executive vice-chairman will be judged upon.
Essex-born and an alumni of the independent Brentwood School where Frank Lampard was also educated, Woodward's first club were his hometown, non-league Chelmsford City. He studied physics at Bristol University and is generally regarded as having a formidable intellect.
While he was a rising star at United during the final years of the Ferguson-David Gill era, he was based in London and ran the Mayfair office where the club struck their first cycle of commercial partnership deals.
Nowadays, he leaves that operation to his friend and colleague Richard Arnold, managing director, although Woodward is likely to come in on the big deals.
He has a close team around him, including the head of corporate development, Matt Judge, whose dry-sounding title disguises the fact he does much of the preparatory work on transfer deals. Another member of the inner circle is Hemen Tseayo, the club's head of corporate finance.
Woodward has an office at the Carrington training ground and another at Old Trafford but spends most of his time at the former. He has long since relocated from south-west London to Cheshire.
It is Woodward's job to oversee the strategy of the club. It was reported yesterday that he and Mourinho were at loggerheads over a technical director, but if Woodward wants one then any manager under his command will have to accept that eventuality.
In recent years Woodward has asserted himself more in the politics of European football, and by virtue of the power and wealth of his club he has considerable influence at Premier League shareholder meetings.
The criticism of him that recurs is that he can take too much time over decisions, that his natural curiosity about the available options makes him over-deliberate.
At least the time frame for appointing the long-term successor to Mourinho next summer will give him some breathing space. In the past, circumstances have dictated that United have been forced to take whoever is available.
Whoever comes in, there is no prospect of the Glazers changing their mind about the man who is the real king of Old Trafford.
Coveted Pochettino playing it cool as he keeps focus on Spurs
Pochettino tops United list after Mourinho axing
Jim White: 'Good riddance to Mourinho and his anti-United football'
Sense of euphoria for players after the end of baffling reign
Daniel McDonnell: 'If United are serious about change, some players need to be shown door'
Ewan MacKenna: 'When the brilliance of Mourinho is perceived to have stopped, the man who was once a mad genius can seem merely mad'
Jamie Carragher: 'Pogba needs an attitude change or he'll be next'
Victor Lindelof says he is happy at Manchester United amid interest from Barcelona
Simon Peach Victor Lindelof has reaffirmed his commitment to Manchester United after the defender's agent raised doubts about his future.
€390,000 a week! David de Gea to finally commit future to Manchester United with...
James Ducker David de Gea is finally ready to end the uncertainty over his future by signing a new long-term contract with Manchester United once the club return from their pre-season tour of Australia and Asia.
'Players are influenced far too much by their agents' - Bryan Robson tells Paul Pogba to...
Simon Peach Bryan Robson believes agents have too much influence on modern players and the Manchester United great hopes wantaway midfielder Paul Pogba continues to knuckle down amid ongoing speculation.
€390,000 a week! David de Gea to finally commit future to Manchester... Manchester United
The five urgent issues facing Ole Gunnar Solskjaer and Manchester United Manchester United
'He was enjoying his holiday and it was a mistake' - Rashford defends Lingard over... Manchester United
'Players are influenced far too much by their agents' - Bryan Robson tells... Manchester United
Harry Maguire showing no signs of trying to force move to Manchester United Manchester United

redpajama_set_name: RedPajamaCommonCrawl


Prevention/avoidance of deadlocks is an active research domain that requires to implement diverse control policies to address this problem. In this paper we present a new specialized Petri Net subclass called Binary ordered resources petri net (BORPN) and its main structural properties. Essentially it is an ordinary class constructed from various state machines that share unitary resources in a complex form, which allows branching and joining processes. Its reduced structure of this new class gives advantages that allow analysis of the entire system behavior, being a prohibitive task for large systems because of the complexity and routing algorithms.


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The sites were spectacular, the people were some of the most friendly and knowledgeable we've ever met, the food was awesome and the hotels… what can I say? The Monasterio is truly world-class, the Sanctuary was a real jewel and being upgraded to the Imperial Suite at the Casa Andina Arequipa was an incredible, unexpected treat.
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text:
Евге́ний Ивановский
 Ивановский, Евгений Леонидович (1878—1967) — участник Белого движения на Юге России, полковник Генерального штаба.
 Ивановский, Евгений Филиппович (1918—1991) — советский военачальник, Герой Советского Союза.

redpajama_set_name: RedPajamaWikipedia


From: Christmas gift from my sister K.
Bureau agent Harris Stuyvesant travels to London in 1926 to investigate a suspected British bomber who has been behind several explosions in the States. A shady government official connects him to a WWI veteran, Bennet Grey, who has the ability to sense when someone is deliberately lying. With Grey's assistance, Stuyvesant goes undercover among one of England's elite families.
The historical backdrop to this novel is fairly unique. It is the interwar years in England, and tensions are high in London due to an impending miner's strike. The labor unions and the ruling class are at each other's throats. I appreciated that this was a time period in history that isn't usually covered in fiction.
"The house" is the Hurleigh House, the great estate where Stuyvesant begins his undercover investigation. It is clear that King loves describing great English country houses – this was demonstrated also in Justice Hall, one of her Mary Russell/ Sherlock Holmes books. Hurleigh House has some great character to it though, so I didn't mind the long descriptions too much.
Both K. and I agreed that we weren't sure we liked Harris Stuyvesant at first. K. said she was particularly perturbed by this because she had liked Mary Russell right away in The Beekeeper's Apprentice. But Stuyvesant comes into his own as a character when he goes undercover. King does a good job of writing smart characters. I love getting into their heads as they figure out individuals and group dynamics. Stuyvesant understands how to win over people, how to strategically play a part to get people to respond a certain way. He does make mistakes, but it's still cool to read the intelligence at work.
The ending was not entirely predictable and for that I was glad. Some of the characters are revealed to be different from how Stuyvesant had pegged them.
The book could certainly have been edited down, in my opinion. There's an interesting story there, but it is suffocated in places by too much exposition of the history or the setting. If you've never read Laurie King before, you should start with The Beekeeper's Apprentice, which is one of my all-time favorite books, with one of my favorite literary characters, Mary Russell.
Soon after I started reading this book, I stopped a moment and read "The Shoes That Were Danced to Pieces." In the fairy tale, the princesses wear out their shoes every night and it's a mystery how they do this. So, the king issues an open call for princes to figure it out in three days, the reward being the throne by marriage to the princess. If they don't, they are killed by the king. The princesses in the fairy tale appear to like dancing at this mysterious ball, and don't want their fun being spoiled by any of the princes. A soldier eventually succeeds in solving the mystery and marries the eldest princess.
In Jessica Day George's book, the princesses are cursed by a pact their mother made, to dance every evening at the ball of an underworld king. Due to the underworld magic, they are unable to speak of their curse to anyone. The king does seek the help of other princes, but they are killed by the underworld king's magic, not by the king himself. So the book's retelling of the story makes a whole lot more people sympathetic.
Jessica Day George also supplies the story with some light historical context. The kingdom of the princesses is Westfalin (similar to the historical region of Westphalia in Germany) and terms of address include "Herr" and "Frau". The Catholic Church plays a role too, as the witchcraft comes to the attention of an archbishop.
I liked Jessica Day George's descriptions of the sisters, such as how they describe the last three sisters "the younger set." Some of the twelve were little more than a type: the religious one, the one who plays piano, the vain one. But I particularly liked Poppy, the rebel, who retains a sense of humor throughout the princesses' ordeal. Rose is the sister that is the main character, but Poppy was actually more interesting and I wish she had a bigger part, or even got her own book.
The hero, Galen, distinguishes himself as a man who knits, because as a soldier, you had to learn to make your own socks and clothing. This skill plays an integral part in the story, fabric being a thing that can contain magical properties.
I checked out three books from the library this time, so now I have ten checked out total. I can get away with this amount because my public library allows you to renew twice as long as no one else has a hold on it. So I usually can have a book out for four weeks.
– This is by the same author of Eifelheim, one of my favorite reads of last year, so I'm very excited to read this one. The River of Stars is the name of a doomed spaceship, thus the very sci-fi appearance of the cover.
– I saw the sequel to Veil of Lies in the New Mysteries section. It sounded interesting, but I like to start at the beginning so I trotted back into the stacks to see if the first book was there. I was in luck! Stories set in medieval times tend to attract my interest and I like a good noir.
– My library has a display that is sometimes thematic but usually just whatever the librarians feel like putting up there. I remembered reading a good review of this book. I'd seen the cover picture as a thumbnail image reviews, but never in person: I had thought that the image on the cover was a bird but it's actually an aerial shot of a young woman walking in a red scarf.
I bought this one long ago for my introductory English course when I was a freshman in college. I confess that I've never much cared for reading Shakespeare, or any play for that matter. My best experience of Shakespeare was watching a performance of "The Taming of the Shrew" in Stratford-upon-Avon.
2. The Book of the Dun Cow by Walter Wangerin Jr.
I read this book in high school and liked it, eventually buying a copy at a used-book sale but not reading it again. I hardly remember anything about it now, but I am planning to re-read it this year as part of the Flashback Challenge.
Another book that I bought for a special topics course in college that focused only on the authors Faulkner and Flannery O'Connor. I loved Faulkner's style of writing even if some stuff went over my head. Sanctuary was the least dense of the Faulkner books that I read.
I had to buy this one for a field biology course, which was the best science general education course I could have picked. We got to tromp around outside, learning to identify birds, bird calls, trees and plants. Have I used the book since? Actually, no I don't think I have. But I like to have it around as reference, just in case.
The Redwall series by Brian Jacques were childhood favorites of mine. Traditionally, the ferrets, rats, weasels, etc were evil in the Redwall universe. I remember being excited to read Outcast of Redwall as the main character would be a ferret and that promised something different. Unfortunately, this book would only serve to underscore the determinism of the Redwall universe. The mouse who raises the ferret from birth says in the end "Veil was bad . . . I know that now. Some creatures cannot help being the way they are." I was furious at the ending of the book and it caused my subsequent disenchantment with the series. As an adult, I'm still fond of the series and own seven of them still, but I should probably donate Outcast because of my strong feelings against it.
This book was given to me by my sister, because she knew I liked the movie which stars a young Jena Malone. I can't remember now how the book and movie compare, but I believe the book is a little darker.
For my senior honors project in college, I explored autobiography, memoir and the concept "everyone has a story." I bought this book at the beginning of my exploration. It didn't get used for my paper in the end, but I remember liking it.
Finally, a more recent purchase! I first read this book as a library copy and then bought my own copy last year at a library used book sale. Despite the generic title, this is really a great read. I got to see de los Santos speak at one of the annual National Book Festivals in D.C. and she was funny and personable.
All I can say about these two is why do I still have them around? Both are books that I needed in college (the second is a French exercise book). I don't need them anymore.
In conclusion, what this random selection says about me is that a lot of my collection is still populated by books purchased when I was an undergraduate. I graduated almost six years ago!
Every now and then, I thought it'd be interesting to write a post on books featured in films. I did this once before with the film Next Stop Wonderland (starring Hope Davis).
Pardon me for picking another relatively unknown film, but books feature so prominently in Whit Stillman's Metropolitan that I couldn't resist.
Metropolitan follows a group of young privileged New Yorkers, about the age of college freshmen, as they progress through the debutante season, with particular focus on the after-parties held into the early morning hours. Giving us an outsider perspective on this kind of life, is a 'West Sider' named Tom who reluctantly lets himself get adopted by this group.
There's a real chemistry within the ensemble that make me feel fond of the characters even when they display unlikable behavior. The dialogue has a wit and vocabulary to die for – it reminds me of dialogue in Austen or Dickens – where it's not really the way that people do talk, but I kind of wish it was.
At this point in the conversation, Tom admits without shame that he actually hasn't read Mansfield Park. "You don't have to read a book to have an opinion on it," he claims. Furthermore, he tells Audrey that he doesn't read novels at all, just literary criticism. The film intends for him to sound ridiculous and he is.
He tells Audrey that he is surprised by how much he is liking it.
The book follows a small group of characters over the course of one night. 19-year-old Mari reads by herself in an all-night diner and meets an acquaintance from the past. This chance meeting indirectly draws Mari into the aftermath of a violent attack on a Chinese prostitute. Meanwhile, Mari's sister Eri deeply sleeps where an unplugged tv ominously flickers.
I was intrigued about this book when I read a review of it in the blog, Book Dilettante. I like movies where events happen over the course of one night, so I liked the idea of After Dark: surreal encounters that occur while most are asleep. Also, I wanted to read a book by Haruki Murakami. I know that The Wind-Up Bird Chronicle and Kafka on the Shore are more known and liked, but After Dark was the one on the shelf at the library.
I liked the weird aspects of the small book, such as the strangeness that surrounds Eri's sleep. Or how a turn in the plot finds an abandoned cell phone ringing in a store, where anyone who picks it up hears threats on their life.
I was disappointed in the book overall, though. I wasn't expecting a grand story, but for a book that is the equivalent of an all-nighter, I was hoping to feel a more coherent mood and atmosphere. I got that in spots, but not as the whole.
Mostly, though, I didn't care for the writing style. I'm thinking a large part of this could be a translation issue. The dialogue didn't feel natural and came off very flat, almost as if the wrong English words were chosen in the effort to approximate casual talk. There were 'gonna's' and "whaddya mean' and stuff like that. And so, so, many 'uh-huh's'. As I've watched some Japanese films, I'm guessing that the 'uh-huh' is a translation of the Japanese quick assenting "hai". I'd rather the 'hai' have been kept, really. I don't think that uh-huh's even accurately capture the English noise of assent.
A different type of stylistic choice that I didn't care for was the 'we' perspective held throughout the book. 'We' are the swooping camera observing these characters, particularly with Eri's scenes, which makes some sense as that character is a model. But I found this perspective to be artificial and cumbersome, rather than thought-provoking.
I'm not going to take this book as an indicator of my future reaction to Murakami and it won't keep me from trying another book of his. If you have read Murakami, recommendations are welcome. Also, has anyone read a translated book and suspected that the translation was inadequate? I'd be interested to hear if others have experienced this.
At first glance, she seemed merely to be gesturing the children to her, but an attentive viewer could not miss the resemblance between woman and weapon: thin, taut, and razor-sharp.


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CAROL COSTELLO, CNN ANCHOR: As we've been telling you for the past several hours, a U.S. Tomahawk missile hit the Iraqi television station in Baghdad. We thought it destroyed it earlier, but apparently it's back on line and running.
We want to head to Amman, Jordan right now and check in with Rula Amin to find out more about this.
It's really no surprise that coalition forces would target this TV station, is it?
RULA AMIN, CNN CORRESPONDENT: No, it's not a surprise. In fact, it's an attack that people here in Amman and in Baghdad thought that it would happen almost on the first night of the war.
The Iraqi leader and the Iraqi government, since the war started, have been using this Iraqi TV very heavily in order to send their message. Saddam Hussein himself was on Iraqi television twice since the war started.
Last night, the Iraqi information minister, Saeed al-Sahaf, was on television reading a letter, a message from the president, Saddam Hussein, to his fighters.
And Iraq has been also using the TV in order to show pictures of what's happening in terms of their fighting and in terms of their resistance. They showed the pictures of the U.S. prisoners. They showed pictures of U.S.-downed planes, Apache helicopters that were downed. They speak -- also they put on the airwaves many Iraqis who say they are willing to fight and dedicate their loyalty to Saddam Hussein.
It's also interesting that the Iraqis were able to go back on air this fast. For them, it's a message of defiance, trying to assure Iraqis, as well as most of the Arab streets who watch Iraqi television though its satellite channel, that the Iraqis are still resisting and they intend to continue doing so -- Carol.
COSTELLO: I was just going to ask you about the sophistication of this TV station, because frankly we were surprised that it's up and running again.
AMIN: Many people have been surprised. Although the Iraqis have been experienced in wars, they have been hit before by wars so many times, whether with the U.S. or with Iran, and probably they have been preparing themselves for such a strike. At the same time, it's very important for them to make sure that their message is still there to all Iraqi people, because they know there's a counter-message coming from the United States trying to rally the Iraqi people for this war, in favor of this war. And the Iraqis are trying to counter it by saying that this attack by the United States, this invasion is not targeting Saddam Hussein only, but targeting Iraqis and Iraq as a nation.
And they're trying to appeal to every sector of the society -- the tribes, the soldiers, the families. That's why when Saddam Hussein is on the air, he always refers to the tribes' name of the Iraqis, and he sometimes mentions even names of fighters who have been able to arrest U.S. soldiers or have downed U.S. planes in order to motivate people to rally around him -- Carol.
COSTELLO: And I know that perhaps the messages coming from Iraqi television has rallied some within Iraq, but how about the rest of the Arab world?
AMIN: Well, you know, it's interesting, Carol, because the Arab world on the first night, when they saw the explosions taking place in Baghdad, their most concern was for the Iraqi civilians that this attack is actually destroying Baghdad, which is a very old Arab capital. In the minds of a lot of Arab people, it's a place that symbolizes the civilization of the Arab nations. And so many were concerned, many were sad.
Afterwards, when the Iraqi television started showing pictures of their resistance, what they call the resistance, when they started putting pictures of U.S. prisoners of war, when they started putting these pictures on the U.S.-downed planes, people in the Arab street became so focused on Iraqi's resistance, because many people did not expect the Iraqis to be able to resist such a might, the U.S. force, so many soldiers, so many sophisticated weapons.
So many of them also know that many Iraqis are not happy with the regime there, they would like to see it removed. So they were surprised at the level of resistance. And now, they're very focused on it. Every day, you'll see many Arabs just hooked on the television, watching, waiting to hear more news of how the Iraqis are handling this war -- Carol.
COSTELLO: Rula Amin reporting live from Amman, Jordan this morning -- thanks.


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text:
UEFA changes Financial Fair Play rules after Chelsea transfers
Sports Dibenedetto Breaking News Tuesday 24th January 2023 06:10 PM
The moves of Chelsea, the most active team of the interim transfer period in the English Premier League, prompted UEFA.
The London team, which has added Mykhaylo Mudryk, Benoit Badiashile, Noni Madueke, Andrey Santos and David Datro Fofana during the interim transfer period so far, has paid approximately 170 million pounds for these transfers. For Joao Felix, who was rented from Atletico Madrid, a rental fee of 11 million pounds was paid.
In these moves made in the interim transfer, Chelsea preferred to sign long-term contracts with the players. An 8.5-year deal was signed with the last transfer, Mykhaylo Mudryk, 6.5 years with Benoit Badiashile and David Fofana, and 7.5 years with Noni Madueke.
⚽ PREMIER LEAGUE NEWS
In the news in the British press, it was stated that Chelsea plans to choose this strategy and spread the testimonial fees over many years, not to be stuck with the Financial Fair Play rules.
In the news in the BBC, it was stated that UEFA is preparing to change the Financial Fair Play rules so that other clubs do not choose this strategy and will make a regulation that will spread the testimonial fee only to the first 5 years.
In addition, it was stated that there will be no rules for teams to sign longer contracts. It was noted that the new rules will come into effect this summer and will not be applied retrospectively.
Chelsea's expenditures approached 450 million pounds with the moves made during the transfer period and last summer's transfer period.
The article is in Turkish
Tags: UEFA Financial Fair Play rules Chelsea transfers
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text:
Hawkins Brook Trail close to becoming a reality
Fundraising effort needs $75,000 more
by Erin Plummer
MEREDITH — The creation of a new nature trail accessible to all alongside Hawkins Brook is close to becoming a reality with some more fundraising and some more steps needed.
The proposed Laverack Nature Trail at Hawkins Brook is $75,000 away from its fundraising goal after receiving a major donation and a Request for Proposals has been sent out for a builder. The trail is the concept of the Meredith Village Pathways Committee, a subcommittee of the board of selectmen, and has been in the planning stages for a number of years.
The trail will start on Route 25 by Meredith Village Savings Bank and the Seneca Ladd Building with parking on the corner of the Seneca Ladd Building going behind the Meredith Bay Shopping Plaza, and ending at Prescott Park. The trail will be 10 feet wide and mostly hardpack material with different boardwalks by the wetland. Three different observation platforms will out into the wetlands along with educational information.
The trail will be entirely handicap accessible and accessible to wheelchairs, strollers, walkers, and any other mobility device.
Pathways Committee member Liz Lapham said this all started in 2012 after she had gone to a Meredith Rotary Cub meeting to thank the group for their support of the Historical Walking Tour project. She said John Sherman approached her and told her about the wetlands behind the shopping center, saying this could be their next project.
Lapham later went out to the wetland with Andrea Bourn, now the chair of the Pathways Committee, and explored the area on kayak.
"We were pretty astounded to find indeed there was this whole wetlands (area) nobody could see because of the shopping center," Lapham said.
She said as they were leaving the area and were between MVSB and the Seneca Ladd Building they met then MVSB President and CEO Sam Laverack and told him what they were doing.
"He said 'Great idea, I'm all for it," Lapham said.
Since then Laverack has been involved in the effort and agreed to be the honorary chairman of the fundraising committee.
When Laverack retired from MVSB the bank's home company New Hampshire Mutual Bank Corp made a $200,000 donation to the trail in Laverack's name. Because if this contribution, the trail will be known as the Laverack Nature Trail at Hawkins Brook.
"That really said to us this is possible," Lapham said.
The trail effort has been the result of the work of the nine-member committee, who Lapham and Bourn said have been loyal and dedicated to the project since the beginning. Bourn especially gave credit to Lapham and Carol Gerken for their efforts on this. The Vermont-based Timber & Stone, LLC has designed the trail, one of many different nature trails the company has designed throughout the Northeast.
Several members of the committee have held house parties to raise money for the trail. One member has sent letters to different businesses in town asking for contributions. They have received contributions from MVSB, the Rotary, the Meredith Conservation Commission, Altrusa Club, and a number of small gifts. The effort received an $80,000 grant from the New Hampshire Bureau of Trails. As the grant has to be spent this fall they will do something on the project this year.
They also received a $5,000 grant from the New Hampshire Charitable Foundation to get advice from a professional fundraiser. The trail also has the approval of neighboring property owners Crosspoint Associates, which owns the shopping plaza, and Meredith Bay Village.
The project is now $75,000 away from being realized.
The trail will be a 10 minute walk from the Inter-Lakes school campus and will have many educational opportunities. Bourn said the committee met over the summer with Inter-Lakes Superintendent Mary Moriarty, administrators, and teachers from the elementary through the high school level and are preparing a teacher's guide to the trail complex.
"Having a walkable community is one of the goals and connecting with nature we know is good for mind, body, and spirit and walking is good for health," Bourn said.
She said the more people are walking from the docks to the trail the more people are walking by businesses in the village.
Bourn said she has heard about people asking for information about accessible trails in the area.
They are aiming to start building the trail sometime in the coming spring. Proposals will be reviewed in September, which will give the committee an indication of how much the project will cost.
The Pathways Committee will also have a booth at this weekend's Fine Arts and Crafts Festival, where there will be more information and opportunities to donate.
To donate make checks out to the Town of Meredith – HBNT. Send checks and IRA contributions to 41 Main St., Meredith, NH, 03253. For more information contact Andrea Bourn at HawkinsBrookNT@gmail.com.
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The dynamism of the female spirit is on display at this luxury hotel.
Looking for a way to celebrate International Women's Day all month? Sofitel London St James has you covered. The luxury hotel, nestled between St James' Park and London's theatre district, is hosting a unique exhibition of new and recent works by leading international artists renowned for depicting the strength and dynamism of the female spirit.
The International Women's Day exhibition is the result of partnership with leading independent gallery Unit London, who have kindly provided artworks by Ava Moradi, Cécile Plaisance, David Kassan, Hollis Dunlap and more.
Running from 8th – 31st March, the exhibition will offer Sofitel London St James' guests and the London public at large a wonderful opportunity to celebrate the female spirit through art.
If you fancy taking one of these stunning works of art home with you, you'll be happy to know that the official charity partner for the exhibition will be Breast Cancer Care. That means a percentage of the profits from the sale of the works will be donated to support the ongoing work of this national charity that provides care, information and support to those affected by breast cancer.
Our advice? Grab a friend and head down to the exhibition for your dose of female empowerment via art, then top off your visit with a cocktail or two in the hotel's sultry St James bar.