text:
\section{Credits}

This document has been adapted from the instructions for earlier ACL
and NAACL proceedings, including those for ACL 2017 by Dan Gildea and
Min-Yen Kan, NAACL 2017 by Margaret Mitchell, ACL 2012 by Maggie Li
and Michael White, those from ACL 2010 by Jing-Shing Chang and Philipp
Koehn, those for ACL 2008 by Johanna D. Moore, Simone Teufel, James
Allan, and Sadaoki Furui, those for ACL 2005 by Hwee Tou Ng and Kemal
Oflazer, those for ACL 2002 by Eugene Charniak and Dekang Lin, and
earlier ACL and EACL formats. Those versions were written by several
people, including John Chen, Henry S. Thompson and Donald
Walker. Additional elements were taken from the formatting
instructions of the {\em International Joint Conference on Artificial
  Intelligence} and the \emph{Conference on Computer Vision and
  Pattern Recognition}.

\section{Introduction}

The following instructions are directed to authors of papers submitted
to ACL 2018 or accepted for publication in its proceedings. All
authors are required to adhere to these specifications. Authors are
required to provide a Portable Document Format (PDF) version of their
papers. \textbf{The proceedings are designed for printing on A4
paper.}

\section{General Instructions}

Manuscripts must be in two-column format.  Exceptions to the
two-column format include the title, authors' names and complete
addresses, which must be centered at the top of the first page, and
any full-width figures or tables (see the guidelines in
Subsection~\ref{ssec:first}). \textbf{Type single-spaced.}  Start all
pages directly under the top margin. See the guidelines later
regarding formatting the first page.  The manuscript should be
printed single-sided and its length
should not exceed the maximum page limit described in Section~\ref{sec:length}.
Pages are numbered for  initial submission. However, \textbf{do not number the pages in the camera-ready version}.

By uncommenting {\verb|\aclfinalcopy|} at the top of this
 document, it will compile to produce an example of the camera-ready formatting; by leaving it commented out, the document will be anonymized for initial submission.  When you first create your submission on softconf, please fill in your submitted paper ID where {\verb|***|} appears in the {\verb|\def\aclpaperid{***}|} definition at the top.

The review process is double-blind, so do not include any author information (names, addresses) when submitting a paper for review.
However, you should maintain space for names and addresses so that they will fit in the final (accepted) version.  The ACL 2018 \LaTeX\ style will create a titlebox space of 6.35 cm for you when {\verb|\aclfinalcopy|} is commented out.

\subsection{The Ruler}
The ACL 2018 style defines a printed ruler which should be present in the
version submitted for review.  The ruler is provided in order that
reviewers may comment on particular lines in the paper without
circumlocution.  If you are preparing a document without the provided
style files, please arrange for an equivalent ruler to
appear on the final output pages.  The presence or absence of the ruler
should not change the appearance of any other content on the page.  The
camera ready copy should not contain a ruler. (\LaTeX\ users may uncomment the {\verb|\aclfinalcopy|} command in the document preamble.)

\textbf{Reviewers}: note that the ruler measurements do not align well with
lines in the paper -- this turns out to be very difficult to do well
when the paper contains many figures and equations, and, when done,
looks ugly. In most cases one would expect that the approximate
location will be adequate, although you can also use fractional
references ({\em e.g.}, the first paragraph on this page ends at mark $117.5$).

\subsection{Electronically-available resources}

ACL provides this description in \LaTeX2e{} ({\small\tt acl2018.tex}) and PDF
format ({\small\tt acl2018.pdf}), along with the \LaTeX2e{} style file used to
format it ({\small\tt acl2018.sty}) and an ACL bibliography style ({\small\tt acl\_natbib.bst})
and example bibliography ({\small\tt acl2018.bib}).
These files are all available at
\url{http://acl2018.org/downloads/acl18-latex.zip}.

A Microsoft Word template file ({\small\tt acl18-word.docx}) and example submission pdf ({\small\tt acl18-word.pdf})
is available at
\url{http://acl2018.org/downloads/acl18-word.zip}.
We strongly recommend the use of these style files, which have been
appropriately tailored for the ACL 2018 proceedings.

\subsection{Format of Electronic Manuscript}
\label{sect:pdf}

For the production of the electronic manuscript you must use Adobe's
Portable Document Format (PDF). PDF files are usually produced from
\LaTeX\ using the \textit{pdflatex} command. If your version of
\LaTeX\ produces Postscript files, you can convert these into PDF
using \textit{ps2pdf} or \textit{dvipdf}. On Windows, you can also use
Adobe Distiller to generate PDF.

Please make sure that your PDF file includes all the necessary fonts
(especially tree diagrams, symbols, and fonts with Asian
characters). When you print or create the PDF file, there is usually
an option in your printer setup to include none, all or just
non-standard fonts.  Please make sure that you select the option of
including ALL the fonts. \textbf{Before sending it, test your PDF by
  printing it from a computer different from the one where it was
  created.} Moreover, some word processors may generate very large PDF
files, where each page is rendered as an image. Such images may
reproduce poorly. In this case, try alternative ways to obtain the
PDF. One way on some systems is to install a driver for a postscript
printer, send your document to the printer specifying ``Output to a
file'', then convert the file to PDF.

It is of utmost importance to specify the \textbf{A4 format} (21 cm
x 29.7 cm) when formatting the paper. When working with
{\tt dvips}, for instance, one should specify {\tt -t a4}.
Or using the command \verb|\special{papersize=210mm,297mm}| in the latex
preamble (directly below the \verb|\usepackage| commands). Then using
{\tt dvipdf} and/or {\tt pdflatex} which would make it easier for some.

Print-outs of the PDF file on A4 paper should be identical to the
hardcopy version. If you cannot meet the above requirements about the
production of your electronic submission, please contact the
publication chairs as soon as possible.

\subsection{Layout}
\label{ssec:layout}

Format manuscripts two columns to a page, in the manner these
instructions are formatted. The exact dimensions for a page on A4
paper are:

\begin{itemize}
\item Left and right margins: 2.5 cm
\item Top margin: 2.5 cm
\item Bottom margin: 2.5 cm
\item Column width: 7.7 cm
\item Column height: 24.7 cm
\item Gap between columns: 0.6 cm
\end{itemize}

\noindent Papers should not be submitted on any other paper size.
 If you cannot meet the above requirements about the production of
 your electronic submission, please contact the publication chairs
 above as soon as possible.

\subsection{Fonts}

For reasons of uniformity, Adobe's \textbf{Times Roman} font should be
used. In \LaTeX2e{} this is accomplished by putting

\begin{quote}
\begin{verbatim}
\usepackage{times}
\usepackage{latexsym}
\end{verbatim}
\end{quote}
in the preamble. If Times Roman is unavailable, use \textbf{Computer
  Modern Roman} (\LaTeX2e{}'s default).  Note that the latter is about
  10\% less dense than Adobe's Times Roman font.

\begin{table}[t!]
\begin{center}
\begin{tabular}{|l|rl|}
\hline \bf Type of Text & \bf Font Size & \bf Style \\ \hline
paper title & 15 pt & bold \\
author names & 12 pt & bold \\
author affiliation & 12 pt & \\
the word ``Abstract'' & 12 pt & bold \\
section titles & 12 pt & bold \\
subsection titles & 11 pt & bold \\
document text & 11 pt  &\\
captions & 11 pt & \\
abstract text & 11 pt & \\
bibliography & 10 pt & \\
footnotes & 9 pt & \\
\hline
\end{tabular}
\end{center}
\caption{\label{font-table} Font guide. }
\end{table}

\subsection{The First Page}
\label{ssec:first}

Center the title, author name(s), and affiliation(s) across both
columns (or, for the initial submission, \textbf{Anonymous ACL submission} for names and affiliations). Do not use footnotes for affiliations.
Include the paper ID number assigned during the submission process in the header.
Use the two-column format only when you begin the abstract.

\textbf{Title}: Place the title centered at the top of the first page, in
a 15-point bold font. (For a complete guide to font sizes and styles,
see Table~\ref{font-table}) Long titles should be typed on two lines
without a blank line intervening. Approximately, put the title at 2.5
cm from the top of the page, followed by a blank line, then the
author name(s), and the affiliation(s) on the following line. Do not
use only initials for given names (middle initials are allowed). Do
not format surnames in all capitals ({\em e.g.}, use ``Mitchell'' not
``MITCHELL'').  Do not format title and section headings in all
capitals as well except for proper names (such as ``BLEU'') that are
conventionally in all capitals.  The affiliation should contain the
author's complete address, and if possible, an electronic mail
address. Start the body of the first page 7.5 cm from the top of the
page.

The title, author names and addresses should be completely identical
to those entered to the electronic paper submission website in order
to maintain the consistency of author information among all
publications of the conference. If they are different, the publication
chairs may resolve the difference without consulting with you; so it
is in your own interest to double-check that the information is
consistent.

\textbf{Abstract}: Type the abstract at the beginning of the first
column. The width of the abstract text should be smaller than the
width of the columns for the text in the body of the paper by about
0.6 cm on each side. Center the word \textbf{Abstract} above the body of the abstract using the font size and style shown in Table~\ref{font-table}. The abstract should be a concise
summary of the general thesis and conclusions of the paper. It should
be no longer than 200 words. The font size of the abstract text should be as shown in Table~\ref{font-table}.

\textbf{Text}: Begin typing the main body of the text immediately after
the abstract, observing the two-column format as shown in
the present document.
Do not include page numbers in the final version.

\textbf{Indent}: Indent when starting a new paragraph, about 0.4 cm.

\begin{table}
\centering
\small
\begin{tabular}{cc}
\begin{tabular}{|l|l|}
\hline
\textbf{Command} & \textbf{Output}\\\hline
\verb|{\"a}| & {\"a} \\
\verb|{\^e}| & {\^e} \\
\verb|{\`i}| & {\`i} \\
\verb|{\.I}| & {\.I} \\
\verb|{\o}| & {\o} \\
\verb|{\'u}| & {\'u}  \\
\verb|{\aa}| & {\aa}  \\\hline
\end{tabular} &
\begin{tabular}{|l|l|}
\hline
\textbf{Command} & \textbf{ Output}\\\hline
\verb|{\c c}| & {\c c} \\
\verb|{\u g}| & {\u g} \\
\verb|{\l}| & {\l} \\
\verb|{\~n}| & {\~n} \\
\verb|{\H o}| & {\H o} \\
\verb|{\v r}| & {\v r} \\
\verb|{\ss}| & {\ss} \\\hline
\end{tabular}
\end{tabular}
\caption{Example commands for accented characters, to be used in, {\em e.g.}, \BibTeX\ names.}\label{tab:accents}
\end{table}

\subsection{Sections}

\textbf{Headings}: Type and label section and subsection headings in the
style shown on the present document.  Use numbered sections (Arabic
numerals) in order to facilitate cross references. Number subsections
with the section number and the subsection number separated by a dot, in Arabic numerals.
Do not number subsubsections ({\em i.e.}, use \verb|\subsubsection*| instead of \verb|\subsubsection|).

\begin{table*}
\centering
\begin{tabular}{lll}
  output & natbib & previous ACL style files\\
  \hline
  \citep{Gusfield:97} & \verb|\citep| & \verb|\cite| \\
  \citet{Gusfield:97} & \verb|\citet| & \verb|\newcite| \\
  \citeyearpar{Gusfield:97} & \verb|\citeyearpar| & \verb|\shortcite| \\
\end{tabular}
\caption{Citation commands supported by the style file.
  The citation style is based on the natbib package and
  supports all natbib citation commands.
  It also supports commands defined in previous ACL style files
  for compatibility.
  }
\end{table*}

\textbf{Citations}: Citations within the text appear in parentheses
as~\cite{Gusfield:97} or, if the author's name appears in the text
itself, as Gusfield~\shortcite{Gusfield:97}.
Using the provided \LaTeX\ style, the former is accomplished using
{\verb|\cite|} and the latter with {\verb|\shortcite|} or {\verb|\newcite|}.
Collapse multiple citations as in~\cite{Gusfield:97,Aho:72}; this is accomplished with the provided style using commas within the {\verb|\cite|} command, {\em e.g.}, {\verb|\cite{Gusfield:97,Aho:72}|}.
Append lowercase letters to the year in cases of ambiguities.
 Treat double authors as
in~\cite{Aho:72}, but write as in~\cite{Chandra:81} when more than two
authors are involved.

Also refrain from using full citations
as sentence constituents.
We suggest that instead of
\begin{quote}
  ``\cite{Gusfield:97} showed that ...''
\end{quote}
you use
\begin{quote}
``Gusfield \shortcite{Gusfield:97}   showed that ...''
\end{quote}

If you are using the provided \LaTeX{} and Bib\TeX{} style files, you
can use the command \verb|\citet| (cite in text)
to get ``author (year)'' citations.

You can use the command \verb|\citealp| (alternative cite without
parentheses) to get ``author year'' citations (which is useful for
using citations within parentheses, as in \citealp{Gusfield:97}).

If the Bib\TeX{} file contains DOI fields, the paper
title in the references section will appear as a hyperlink
to the DOI, using the hyperref \LaTeX{} package.
To disable the hyperref package, load the style file
with the \verb|nohyperref| option:
\verb|\usepackage[nohyperref]{acl2018}|.

\textbf{Compilation Issues}: Some of you might encounter the following error during compilation:

``{\em \verb|\pdfendlink| ended up in different nesting level than \verb|\pdfstartlink|.}''

This happens when \verb|pdflatex| is used and a citation splits across a page boundary. To fix this, disable the \verb|hyperref| package (see above), recompile and see the problematic citation.
Next rewrite that sentence containing the citation. (See, {\em e.g.}, {\small\tt http://tug.org/errors.html})

\textbf{Digital Object Identifiers}:  As part of our work to make ACL
materials more widely used and cited outside of our discipline, ACL
has registered as a CrossRef member, as a registrant of Digital Object
Identifiers (DOIs), the standard for registering permanent URNs for
referencing scholarly materials. We are requiring all
camera-ready references to contain the appropriate DOIs (or as a
second resort, the hyperlinked ACL Anthology Identifier) to all cited
works.  Thus, please ensure that you use Bib\TeX\ records that contain
DOI or URLs for any of the ACL materials that you reference.
Appropriate records should be found for most materials in the current
ACL Anthology at \url{http://aclanthology.info/}.

As examples, we cite \cite{P16-1001} to show you how papers with a DOI
will appear in the bibliography.  We cite \cite{C14-1001} to show how
papers without a DOI but with an ACL Anthology Identifier will appear
in the bibliography.

As reviewing will be double-blind, the submitted version of the papers
should not include the authors' names and affiliations. Furthermore,
self-references that reveal the author's identity, {\em e.g.},
\begin{quote}
``We previously showed \cite{Gusfield:97} ...''
\end{quote}
should be avoided. Instead, use citations such as
\begin{quote}
``\citeauthor{Gusfield:97} \shortcite{Gusfield:97}
previously showed ... ''
\end{quote}

\textbf{Please do not use anonymous citations} and do not include
acknowledgments when submitting your papers. Papers that do not
conform to these requirements may be rejected without review.

\textbf{References}: Gather the full set of references together under
the heading \textbf{References}; place the section before any Appendices,
unless they contain references. Arrange the references alphabetically
by first author, rather than by order of occurrence in the text.
Provide as complete a citation as possible, using a consistent format,
such as the one for {\em Computational Linguistics\/} or the one in the
{\em Publication Manual of the American
Psychological Association\/}~\cite{APA:83}.  Use of full names for
authors rather than initials is preferred.
A list of abbreviations for common computer science journals can be found in the ACM {\em Computing Reviews\/}~\cite{ACM:83}.

The \LaTeX{} and Bib\TeX{} style files provided roughly fit the
American Psychological Association format, allowing regular citations,
short citations and multiple citations as described above.

\textbf{Appendices}: Appendices, if any, directly follow the text and the
references (but see above).  Letter them in sequence and provide an
informative title: \textbf{Appendix A. Title of Appendix}.

\subsection{Footnotes}

\textbf{Footnotes}: Put footnotes at the bottom of the page and use the footnote font size shown in Table~\ref{font-table}. They may be numbered or referred to by asterisks or other
symbols.\footnote{This is how a footnote should appear.} Footnotes
should be separated from the text by a line.\footnote{Note the line
separating the footnotes from the text.}

\subsection{Figures and Tables}

\textbf{Placement}: Place figures and tables in the paper near where they are first discussed, as close as possible to the top of their respective column.

\textbf{Captions}: Provide a caption for every illustration; number each one
sequentially in the form:  ``Figure 1: Caption of the Figure.'' ``Table 1:
Caption of the Table.''  Type the captions of the figures and
tables below the body, using the caption font size shown in Table~\ref{font-table}.

\subsection{Equation}
\label{ssec:eqn}

An example equation is shown below:
\begin{equation}
A=\pi r^2
\end{equation}

The numbering (if any) and alignment of the equations will be done automatically (using \verb|align| or \verb|equation|).

\subsection{Accessibility}
\label{ssec:accessibility}

In an effort to accommodate the color-blind (as well as those printing
to paper), grayscale readability for all accepted papers will be
encouraged.  Color is not forbidden, but authors should ensure that
tables and figures do not rely solely on color to convey critical
distinctions.
A simple criterion: All curves and points in your figures should be clearly distinguishable without color.

\section{Translation of non-English Terms}

It is also advised to supplement non-English characters and terms
with appropriate transliterations and/or translations
since not all readers understand all such characters and terms.
Inline transliteration or translation can be represented in
the order of: original-form transliteration ``translation''.

\section{Length of Submission}
\label{sec:length}

The ACL 2018 main conference accepts submissions of long papers and
short papers.
 Long papers may consist of up to eight (8) pages of
content plus unlimited pages for references. Upon acceptance, final
versions of long papers will be given one additional page -- up to nine (9)
pages of content plus unlimited pages for references -- so that reviewers' comments
can be taken into account. Short papers may consist of up to four (4)
pages of content, plus unlimited pages for references. Upon
acceptance, short papers will be given five (5) pages in the
proceedings and unlimited pages for references.

For both long and short papers, all illustrations and tables that are part
of the main text must be accommodated within these page limits, observing
the formatting instructions given in the present document. Supplementary
material in the form of appendices does not count towards the page limit.

However, note that supplementary material should be supplementary
(rather than central) to the paper, and that reviewers may ignore
supplementary material when reviewing the paper (see Appendix
\ref{sec:supplemental}). Papers that do not conform to the specified
length and formatting requirements are subject to be rejected without
review.

Workshop chairs may have different rules for allowed length and
whether supplemental material is welcome. As always, the respective
call for papers is the authoritative source.

\section*{Acknowledgments}

The acknowledgments should go immediately before the references.  Do not number the acknowledgments section ({\em i.e.}, use \verb|\section*| instead of \verb|\section|). Do not include this section when submitting your paper for review.

\section{Introduction}

Distributed representations of knowledge base entities and concepts have become
key elements of many recent NLP systems, for applications from document ranking
\cite{Jimeno-Yepes2015} and knowledge base completion \cite{Toutanova2015} to
clinical diagnosis code prediction \cite{Choi2016, Choi2016b}. These works have
taken two broad tacks for the challenge of learning to represent entities, each
of which may have multiple unique surface forms in text.
Knowledge-based approaches learn entity representations based on the structure
of a large knowledge base, often augmented by annotated text resources
\cite{Yamada2016,Cao2017}. Other methods utilize explicitly annotated
data, and have been more popular in the biomedical domain \cite{Choi2016,
Mencia2016}. Both approaches, however, are often limited by ignoring some or
most of the available textual information. Furthermore, such rich structures
and annotations are lacking for many specialized domains, and can be
prohibitively expensive to obtain.

We propose a fully text-based method for jointly learning representations of
words, the surface forms of entities, and the entities themselves, from an
unannotated text corpus. We use distant supervision from a
\textit{terminology}, which maps entities to known surface forms.  We augment
the well-known log-linear skip-gram model \cite{Mikolov2013a} with additional
term- and entity-based objectives, and evaluate our learned embeddings in both
intrinsic and extrinsic settings.

Our joint embeddings clearly outperform prior entity embedding methods on
similarity and relatedness evaluations. Entity and word embeddings
capture complementary information, yielding improved performance when they
are combined. Analogy completion results further illustrate these
differences, demonstrating that entities capture domain knowledge,
while word embeddings capture morphological and lexical information. Finally,
we see that an oracle combination of entity and text embeddings nearly matches
a state of the art unsupervised method for biomedical word sense
disambiguation that uses complex knowledge-based approaches.
However, our embeddings show a significant
drop in performance compared to prior work in a newswire disambiguation dataset,
indicating that knowledge graph structure contains entity information
that a purely text-based approach does not capture.

\section{Related Work}

Knowledge-based approaches to entity representation are well-studied in recent literature.
Several approaches have learned representations from knowledge graph structure alone \cite{Grover2016,
Yang2016b,Wang2017}.  \newcite{Wang2014b}, \newcite{Yamada2016}, and \newcite{Cao2017} all use a joint embedding method,
learning representations of text from a large corpus and entities from a knowledge graph; however, they rely
on the disambiguated entity annotations in Wikipedia to align their models.  \newcite{Fang2016} investigate
heuristic methods for joint embedding without annotated entity mentions, but still rely on graph
structure for entity training.

The robust terminologies available in the biomedical domain
have been instrumental to several recent annotation--based
approaches.  \newcite{DeVine2014} use string matching heuristics to find possible
occurrences of known biomedical concepts in literature abstracts, and use the sequence of these noisy
concepts (without the document text) as input for skip-gram training.
\newcite{Choi2016CRI} and
\newcite{Choi2016} use sequences of structured medical observations from patients' hospital stays
for context-based learning.  Finally, \newcite{Mencia2016} take documents tagged with Medical Subject Heading
(MeSH) topics, and use their texts to learn representations of the MeSH headers.  These methods are
able to draw on rich structured and semi-structured data from medical databases, but discard important textual
information, and empirically are limited in the scope of the vocabularies they can embed.

\section{Methods}

In order to jointly learn entity and text representations from an unannotated corpus, we use
distant supervision \cite{Mintz2009} based on known {\it terms},
strings which can represent one or more entities.  The mapping between terms and entities
is many-to-many; for example, the same infection can be expressed as ``cold'' or ``acute rhinitis'', but ``cold''
can also describe the temperature or refer to chronic obstructive lung disease.

Mappings between terms and entities are defined by a terminology.\footnote{
    {\it Terminology} is overloaded with both biomedical and lexical senses; we use it
    here strictly to mean a mapping between terms and entities.
}  We extracted terminologies from two well-known knowledge bases:

\textbf{The Unified Medical Language System} (UMLS; \citealp{Bodenreider2004});
we use the mappings between concepts and strings in the MRCONSO table
as our terminology.  This yields 3.5 million entities, represented by
7.6 million strings in total.

\textbf{Wikipedia}; we use page titles and redirects as our terminology.
This yields 9.7 million potential entities (pages), represented by 17.1 million
total strings.
Table~\ref{tbl:terminologies} gives further statistics about the mapping
between entities and surface forms in each of these terminologies.

\input{tbls/tbl-terminologies}

While iterating through the training corpus, we identify any exact matches of the terms in our terminologies.\footnote{
    We lowercase and strip special characters and punctuation from both terms and corpus
    text, and then find all exact matches for the terms.
}  We allow for overlapping terms: thus, ``in New
York City'' will include an occurrence of both the terms ``New York'' and ``New York
City.'' Each matched term may refer to one or more entities; we do not use a disambiguation model
in preprocessing, but rather assign
a probability distribution over the possible entities.

\subsection{Model}

We extend the skip-gram model of \newcite{Mikolov2013a},
to jointly learn vector representations of words, terms, and entities from
shared textual contexts.
For a given target word, term, or entity $v$, let $C_v = c_{-k}\dots c_{k}$ be
the observed contexts in a window of $k$ words to the left and right of $v$,
and let $N_v = n_{-k,1}\dots n_{k,d}$ be the $d$ random negative samples for
each context word.
Then, the context-based objective for training $v$ is

\vspace{-0.33cm}
{\small
\begin{equation}
    \label{eq:word-objective}
    O(v,C_v,N_v) = \sum_{c \in C_v}\textrm{log}\sigma(\vec{c}\cdot\vec{v}) + \sum_{n \in N_v}\textrm{log}\sigma(-\vec{n}\cdot\vec{v})
\end{equation}
}

\vspace{-0.33cm}
\noindent where $\sigma$ is the logistic function.

We use a sliding context window to iterate through our corpus.  At each
step, the word $w$ at the center of the window $C_w$ is updated using $O(w,C_w,N_w)$,
where $N_w$ are the randomly-selected negative samples.

As terms are of variable token length, we treat each term $t$ as an atomic unit for training,
and set $C_t$ to be the context words prior to the first token of the term and following the final
token.  Negative samples $N_t$ are sampled independently of $N_w$.

Finally, each term $t$ can represent a set of entities $E_t$.  Vectors for these entities are updated
using the same $C_t$ and $N_t$ from $t$. Since the entities are latent, we weight updates
with uniform probability $|E_t|^{-1}$; attempts to learn this probability did not produce qualitatively
different results from the uniform distribution.
Thus, letting $T$ be the set of terms completed at $w$,
the full objective function to maximize is:

\vspace{-0.33cm}
{\small
\begin{equation}
    \label{eq:objective}
    \begin{split}
        \hat{O} =\ &O(w,C_w,N_w) + \\
            &\sum_{t \in T}\Big[O(t,C_t,N_t) + \sum_{e \in E_t}\frac{1}{|E_t|}O(e,C_t,N_t)\Big]
    \end{split}
\end{equation}
}

\vspace{-0.33cm}
Term and entity
updates are only calculated when the final token of one or more terms is reached; word updates are
applied at each step. To assign more weight to near contexts, we subsample the window
size at each step from $[1,k]$.

\subsection{Training corpora}
\input{tbls/tbl-corpus-polysemys}

We train embeddings on three corpora.  For our biomedical embeddings, we use
2.6 billion tokens of biomedical abstract texts from the 2016 PubMed baseline (1.5 billion
noisy annotations).
For comparison to previous open-domain work, we use English Wikipedia (5.5
million articles from the 2018-01-20 dump); we also use the Gigaword 5 newswire
corpus \cite{Gigaword5}, which does not have gold entity annotations.

As our model does not include a disambiguation module for handling ambiguous
term mentions, we also calculate the expected effect of polysemous terms on
each entity that we embed using a given corpus. We call this the
entity's \textit{corpus polysemy}, and denote it with $CP(e)$. For entity $e$
with corresponding terms $T_e$, $CP(e)$ is given as
\begin{equation}
    CP(e) = \sum_{t\in T_e}\frac{f(t)}{Z}\textrm{polysemy}(t)
\end{equation}
\noindent where $f(t)$ is the corpus frequency of term $t$, $Z$ is the
frequency of all terms in $T_e$, and polysemy$(t)$ is the number of
entities that $t$ can refer to.

Table~\ref{tbl:corpus-polysemys} breaks down expected polysemy impact for
each corpus. The vast majority of entities experience some polysemy
effect in training, but very few have an average ambiguity per mention
of 50\% or greater. Most entities with high corpus polysemy
are due to a few highly ambiguous generic strings, such as
\textit{combinations} and \textit{unknown}. However, some specific terms
are also high ambiguity: for example, \textit{Washington County} refers to 30
different US counties.

\subsection{Hyperparameters}

For all of our embeddings, we used the following hyperparameter settings:
a context window size of 2, with 5 negative samples per word; initial
learning rate of 0.05 with a linear decay over 10 iterations through the
corpus; minimum frequency for both words and terms of 10, and a subsampling
coefficient for frequent words of 1e-5.

\subsection{Baselines}

We compare the words, terms,\footnote{
    Unknown terms were handled by backing off to words.
} and entities learned in our model
against two prior biomedical embedding methods, using pretrained embeddings
from each. \newcite{DeVine2014} use sequences of automatically identified
ambiguous entities for skip-gram training,
and \newcite{Mencia2016} use texts of documents tagged with MeSH headers to
represent the header codes.  The most recent comparison method for Wikipedia entities is
MPME \cite{Cao2017}, which uses link anchors and graph structure to augment
textual contexts.  We also include skip-gram vectors as a final baseline;
for Pubmed, we use pretrained embeddings with optimized hyperparameters from
\newcite{Chiu2016b}, and we train our own embeddings with word2vec for both
Wikipedia and Gigaword.

\section{Evaluations}
\input{tbls/tbl-simrel-umnsrs}

Following \newcite{Chiu2016a}, \newcite{Cao2017}, and others, we evaluate our
embeddings on both intrinsic and extrinsic tasks. To evaluate the semantic
organization of the space, we use the standard intrinsic evaluations of
similarity and relatedness and analogy completion. To explore the applicability
of our embeddings to downstream applications, we apply them to named entity
disambiguation. Results and analyses for each experiment are discussed
in the following subsections.

\subsection{Similarity and relatedness}

We evaluate our biomedical embeddings on  the UMNSRS datasets
\cite{Pakhomov2010}, consisting of pairs of UMLS
concepts with judgments of similarity (566 pairs) and relatedness (587 pairs),
as assigned by medical experts.  For evaluating our Wikipedia entity
embeddings, we created WikiSRS, a novel dataset of similarity and relatedness
judgments of paired Wikipedia entities (people, places, and organizations),
as assigned by Amazon Mechanical Turk workers.  We followed the design
procedure of \citet{Pakhomov2010} and produced 688 pairs each of similarity
and relatedness judgments; for further details on our released dataset, please
see the Appendix.

For each labeled entity pair, we calculated the cosine similarity of their
embeddings, and ranked the pairs in order of descending similarity.  We
report Spearman's $\rho$ on these rankings as compared to the ranked human
judgments: Table~\ref{tbl:simrel-umnsrs} shows
results for UMNSRS, and Table~\ref{tbl:simrel-wikisrs} for WikiSRS.

As the dataset includes both string and disambiguated entity forms for each
pair, we evaluate each type of embeddings
learned in our model. Additionally, as words and entities are embedded in
the same space (and thus directly comparable), we experiment with two
methods of combining their information. Entity+Word sums
the cosine similarities calculated between the entity embeddings
and word embeddings for each pair; the Cross setting further adds
comparisons of each entity in the pair to the string form of the other.

\subsubsection{Results}

\input{tbls/tbl-simrel-wikisrs}

Our proposed method clearly outperforms prior work and text-based baselines on
both datasets.  Further, we see that the words and entities
learned by our model include complementary information, as combining them
further increases our ranking performance by a large margin.
As the results on UMNSRS could have been due to our model's ability to embed
many more entities than prior methods, we also filtered the
dataset to the 255 similarity pairs and 260 relatedness pairs that all
evaluated entity-level methods could represent;\footnote{
    For WikiSRS, all methods covered all pairs.
} Table~\ref{tbl:simrel-umnsrs}
shows similar gains on this even footing.
We follow \newcite{Rastogi2015} in
calculating significance, and use their statistics to estimate the minimum
required difference for significant improvements on our datasets.

\input{tbls/tbl-simrel-methods}

In UMNSRS, we found that cosine similarity of entities consistently reflected
human judgments of similarity better than of relatedness; this reflects
previous observations by \newcite{Agirre2009} and \newcite{Muneeb2015}.
Interestingly, we see the opposite behavior in WikiSRS, where relatedness is
captured better than similarity in all settings. In fact, we see a number of
errors of relatedness in WikiSRS predictions, e.g., ``Hammurabi I'' and
``Syria'' are marked highly similar, while the composers ``A.R. Rahman'' and
``John Phillip Sousa'' are marked dis-similar.  MPME embeddings tend towards
over-relatedness as well (e.g., ranking ``Richard Feynman'' and
``Paris-Sorbonne University'' much more highly than gold labels). Despite better
similarity performance, this trend of over-relatedness also holds in biomedical
embeddings: for example, \ent{C0027358} (Narcan) and \ent{C0026549}
(morphine) are consistently marked highly similar across embedding methods,
even though Narcan blocks the effects of opioids like morphine.

\subsubsection{Comparing entities and words}

We observe clear differences in the rankings made by entity vs word
embeddings. As shown in Table~\ref{tbl:simrel-methods}, highly related
entities tend to have high cosine similarity, while word embeddings are more
sensitive to lexical overlap and direct cooccurrence. Combining both sources
often gives the most inuitive results, balancing lexical effects with
relatedness. For example, while the top three pairs by combination in WikiSRS
are likely to co-occur, the top three in UMNSRS are pairs of drug
choices (antibiotics, ACE inhibitors, and chemotherapy drugs, respectively),
only one of which is likely to be prescribed to any given patient at once.

These differences also play out in erroneous predictions. Entity embeddings
often fix the worst misrankings by words: for example, ``Tony Blair'' and
``United Kingdom'' (gold rank: 28) are ranked highly unrelated (position
633) by words, but entities move this pair back up the list (position 86).
However, errors made by entity embeddings are often also made by words:
e.g., \ent{C0011175} (dehydration) and \ent{C0017160} (gastroenteritis) are
erroneously ranked as highly unrelated by both methods. Interestingly, we
find no correlation between the corpus polysemy of entity pairs and
ranking performance, indicating that ambiguity of term mentions
is not a significant confound for this task.

\subsection{Analogy completion}

We use analogy completion to further explore the properties of our joint
embeddings. Given analogy $a:b::c:d$, the task is to guess $d$ given $(a,b,c)$,
typically by choosing the word or entity with highest cosine similarity to
$b - a + c$ \cite{Levy2014}. We report accuracy using the top guess
(ignoring $a,b$, and $c$ as candidates, per \citealp{Linzen2016}).

\subsubsection{Biomedical analogies}

To compare between word and entity representations, we use the entity-level
biomedical dataset BMASS \cite{Newman-Griffis2017}, which includes both entity
and string forms for each analogy. In order to test if words and entities are
capturing complementary information, we also include an oracle evaluation, in
which an analogy is counted as correct if either words or entities produce a
correct response.\footnote{
    We use the Multi-Answer setting for our evaluation (a single $(a,b,c)$
    triple, but a set of correct values for $d$).
} We do not compare against prior biomedical
entity embedding methods on this dataset, due to their limited vocabulary.

\input{tbls/tbl-bmass-results}

Table~\ref{tbl:bmass-results} contrasts the performance of different
jointly-trained representations for five relations with the largest performance
differences from this dataset. For \textit{gene-encodes-product} and
\textit{refers-to}, both of which require structured domain knowledge, entity
embeddings significantly outperform word-level representations. Many of the
errors made by word embeddings in these relations are due to lexical
over-sensitivity: for example, in the renaming analogy \textit{spinal
epidural hematoma:epidural hemorrhage::canis familiaris:\underline{\phantom{dog}}},
words suggest latinate completions such as \textit{latrans} and
\textit{caballus}, while entities capture the correct \ent{C1280551} (dog).
However, on more morphological relations such as
\textit{has-free-acid-or-base-form}, words are by far the better option.

The success of the oracle combination method for entity and word predictions
clearly indicates that not only are words and entities capturing different
knowledge, but that it is complementary. In the majority of the 25 relations
in BMASS, oracle results improved on words and entities alone by at
least 10\% relative. In some cases, as with
\textit{has-free-acid-or-base-form}, one method does most of the heavy
lifting. In several others, including the challenging (and open-ended)
\textit{associated-with}, entities and words capture nearly orthogonal cases,
leading to large jumps in oracle performance.

\subsubsection{General-domain analogies}

No entity-level encyclopedic analogy dataset is available, so we follow
\newcite{Cao2017} in evaluating the effect of joint training on words using the Google analogy
set \cite{Mikolov2013a}.  As shown in Table~\ref{tbl:google-results},
our Wikipedia embeddings roughly match MPME embeddings (which use annotated entity links)
on the semantic portion of the dataset, but our ability to train on unannotated Gigaword
boosts our results on all relations except \textit{city-in-state}.\footnote{
    We failed to precisely replicate the analogy numbers reported by \newcite{Cao2017};
    we attribute this primarily to the different training corpus and slightly different
    preprocessing.
}  Overall, we find that jointly-trained word embeddings split performance
with word-only skipgram training, but that word-only training tends to get
consistently closer to the correct answer. This suggests that
terms and entities may conflict with word-level semantic signals.

\subsection{Entity disambiguation}

Finally, to get a picture of the impact of our embedding method on downstream
applications, we investigated entity disambiguation.\footnote{
    This task is also referred to as entity linking and entity
    sense disambiguation.
} Given a named entity occurrence in context, the task is to assign a canonical
identifier to the entity being referred to: e.g., to mark that ``New York''
refers to the city in the sentence, ``The mayor of New York held a press
conference.'' It bears noting that in unambiguous cases, a terminology alone
is sufficient to link the correct entity: for example, ``Barack Obama'' can only
refer to a single entity, regardless of context. However, many entity strings (e.g.,
``cold'', ``New York'') are ambiguous, necessitating the use of alternate sources
of information such as our embeddings to assign the correct entity.

\subsubsection{Biomedical abstracts}
\label{sssec:msh-wsd}

\input{tbls/tbl-analogy-results}

We evaluate on the MSH WSD dataset \cite{Jimeno-Yepes2011}, a
benchmark for biomedical word sense disambiguation. MSH WSD consists of mentions
of 203 ambiguous terms in biomedical literature, with over 30,000 total
instances. Each sample is annotated with the set of UMLS entities the term
could refer to. We adopt the unsupervised method of
\newcite{Sabbir2017}, which combines cosine similarity and projection
magnitude of an entity representation $e$ to the averaged word embeddings of its
contexts $C_{avg}$ as follows:
\begin{equation}
    \label{eq:msh-wsd}
    f(e,C_{avg}) = \mathrm{cos}(C_{avg},e)\cdot \frac{||P(C_{avg},e)||}{||e||}
\end{equation}
The entity maximizing this score is predicted.

We compare against concept embeddings learned by \newcite{Sabbir2017}. They used
MetaMap \cite{Aronson2010} with the disambiguation module enabled on a curated
corpus of 5 million Pubmed abstracts to create a UMLS concept cooccurrence
corpus for word2vec training. As shown in Table~\ref{tbl:msh-wsd-results}, our
method lags behind theirs, though it clearly beats both random (49.7\%
accuracy) and majority class (52\%) baselines. In addition, we leverage our
jointly-embedded entities and words by adding in the definition-based model
used by \newcite{Pakhomov2016}, which calculates an entity's embedding as the
average of definitions of its neighbors in the UMLS hierarchy
\cite{McInnes2011}.  We use this alternate entity embedding in
Equation~\ref{eq:msh-wsd} to calculate a second score that we add to the direct
entity embedding score.  This yields a large performance boost of over 6\%
absolute, indicating that using entities and words together makes up much of
the gap between our distantly supervised embeddings and the external resources
used by \newcite{Sabbir2017}. Using the definition-based method alone with our
jointly-embedded words, we see a significant increase over \newcite{Pakhomov2016},
indicating the benefits of joint training. However, the combined
entity and definition model still yields a significantly different 2\% boost
in accuracy over definitions alone. Finally, we evaluate an oracle combination that
reports correct if either entity or definition embeddings achieve the correct
result; as shown in the last row of Table~\ref{tbl:msh-wsd-results}, this
combination outperforms the entity-only method of \newcite{Sabbir2017}, and
approaches their state-of-the-art result that combines entity embeddings
with a knowledge-based approach from the structure of the UMLS.

\input{tbls/tbl-msh-wsd-results}

Specific errors shed more light on these differences. The definition-based
method performs better in many cases where the surface form is a common word,
such as {\it coffee} (68\% definition accuracy vs 28\% entity accuracy) and
{\it iris} (93\% definition accuracy vs 35\% entity accuracy). Entities
outperform on some more technical cases, such as {\it potassium} (74\% entity
accuracy vs 49\% definition accuracy). Combining both approaches in the joint
model recovers performance on several cases of low entity accuracy; for example,
joint accuracy on {\it coffee} is 68\%, and on {\it lupus} (53\% entity accuracy),
joint performance is 60\%.

\subsubsection{Newswire entities}

AIDA \cite{Hoffart2011} is a standard dataset for entity linking in newswire,
consisting of approximately 30,000 entities linked to Wikipedia page IDs.
To reduce the search space, \newcite{Pershina2015} provided a set of candidate
entities for each mention, which we use for our experiments.
The MPME model of \newcite{Cao2017} achieves near state-of-the-art performance
accuracy on AIDA with this candidate set, using the mention sense distributions
and full document context included in the model. As our embeddings are trained
without explicit entity annotations, we instead use the same cosine similarity
and projection model discussed in Section~\ref{sssec:msh-wsd} for this task.
In contrast to our results on the biomedical data, we see performance far below
the baseline on these data, as shown in Table~\ref{tbl:aida-results}.

\input{tbls/tbl-aida-results}

\input{tbls/tbl-poly-neighbors}

However, we improve this performance slightly by multiplying by the
similarity between the entity embedding and the average word embedding of
the mention itself; this gives us roughly a further 4\% accuracy for both
Wikipedia and Gigaword embeddings. Using the surface form recovers several
cases where entities alone yield unlikely options, e.g. Roman-era Britain
instead of the United Kingdom for {\it Britain}. However, it also introduces
lexical errors: for example, {\it British}
\input{figures/fig-neighbor-sty}
in several cases refers to the United Kingdom, but the British people are
often selected instead. We note that this extra
score actually hurts performance on MSH WSD, where the terms are curated to be
highly ambiguous, in contrast to the shorter contexts and
clearer terms used in AIDA.

Two other issues bear consideration in this evaluation. Prior approaches to
the AIDA dataset, including MPME, make use of the global context of entity
mentions within a document to improve predictions; by using local context only,
we observe some inconsistent predictions, such as selecting the cricket world
cup instead of the FIFA competition for {\it world cup}, in a document discussing
football. Additionally, in contrast to the MSH WSD dataset, many instances in AIDA
have several highly-related candidates that introduce some confusion in our results.
For example, {\it Ireland} could refer to the United Kingdom of Great Britain and
Ireland, the island of Ireland, or the Republic of Ireland. As our embedding training
does not include gold entity links, cases like this are often errors in our predictions.

\section{Analysis of joint embeddings}

To get a more detailed picture of our joint embedding
space, we investigate nearest neighbors for each point by cosine similarity.
As entities in the UMLS are assigned one or more of over 120 semantic types, we first
examine how intermixed these types are in our biomedical embeddings.
Figure~\ref{fig:neighbor-sty} shows how often an entity's nearest neighbor
shares at least one semantic type with it, across the three
biomedical embedding methods we evaluated. As each set of embeddings has a
different vocabulary, we also restrict to the entities that all three can
embed (approximately 11,000).

We see that our method puts entities of the same type together nearly 40\%
of the time, despite embedding over 270 thousand entities. On an even footing,
our method puts types together significantly more often \newcite{Mencia2016}
(McNemar's; $p<0.05$), and equivalently with \newcite{DeVine2014},
despite using less entity-level information in training.
Within our embeddings, major biological types such as bacteria, eukaryotes,
mammals, and viruses all have more than 60\% of neighbors with the same type,
while less structured clinical types such as Clinical Attribute and Daily or
Recreational Activity are in the 10-20\% range. Corpus polysemy does not appear
to have any effect on this type matching (mean polysemy of 1.5 for both matched
and non-matched entities).

Expanding to include the words and terms in the joint embedding space, however,
we see definite qualitative effects of corpus polysemy on entity nearest neighbors.
Table~\ref{tbl:poly-neighbors} gives nearest word, term, entity, and joint
neighbors to two biomedical entities: \ent{C0009443} (the common cold; $CP=6.71$)
and \ent{C0242797} (home health aides; $CP=1$). For the more polysemous
\ent{C0009443}, where 95\% of its mentions are of the word ``cold'' (polysemy=7),
word-level neighbors are mostly nonsensical, while term neighbors are more
logical, and entity neighbors reflect different senses of ``cold''. By contrast,
the non-polysemous \ent{C0242797}, which is represented by 14 different
unambiguous strings, words, terms, and entities are all very clearly in line
with the theme of home health aides. Notably, the common and unambiguous terms
for \ent{C0242797} are its nearest neighbors out of all points, while only two
of the top 10 neighbors to \ent{C0009443} are terms.

\section{Discussion}

\newcite{Faruqui2016} observe that similarity and relatedness are not clearly
distinguished in semantic embedding evaluations, and that it is unclear exactly
how vector-space models should capture them.  We see more evidence of this,
as cosine similarity seems to be capturing a mix of
the two properties in our data.  This mix is clearly informative, but it
empirically favors
relatedness judgments, and cosine similarity is insufficient to separate
the two properties.

Corpus polysemy plays a qualitative role in our embedding model, but
less of a quantitative one. It does not correlate with similarity
and relatedness judgments or entity disambiguation decisions, but it clearly
affects the organization of the embedding space, by embedding
entities with high corpus polysemy in less coherent areas than those with low
polysemy. \newcite{Linzen2016} points out that for analogy completion, local
neighborhood structure can interfere with standard methods; how this
neighborhood structure affects predictions in more complex
tasks is an open question.

Overall, we find two main advantages to our model over prior work. First,
by only using a terminology and an unannotated corpus, we are able to learn
entity embeddings from larger and more diverse data; for example, embeddings
learned from Gigaword (which has no entity annotations) outperform embeddings
learned on Wikipedia in most of our experiments. Second, by embedding entities
and text into a joint space, we are able to leverage complementary information
to get higher performance in both intrinsic and extrinsic tasks; an oracle
model nearly matches
a state-of-the-art ensemble vector and knowledge-based model for biomedical
word sense disambiguation. However,
our other entity disambiguation results demonstrate that there is additional
entity-level information that we are not yet capturing. In particular, it
is unclear whether our low performance on disambiguating newswire entities
is due to a disambiguation model mismatch, a lack of information in our
embeddings, or a combination of both.

\section{Conclusions}

We present a method for jointly learning embeddings of entities and text from
an arbitrary unannotated corpus, using only a terminology for distant supervision.
Our learned embeddings better capture both biomedical and encyclopedic similarity and
relatedness than prior methods, and approach state-of-the-art performance for
unsupervised biomedical word sense disambiguation. Furthermore, entities and
words learned jointly with our model capture complementary information, and
combining them improves performance in all of our evaluations.
We make an implementation of our method available at
{\tt github.com/OSU-slatelab/JET}, along with
the source code used for our evaluations and our pretrained entity
embeddings.
Our novel Wikipedia
similarity and relatedness datasets are available at the same source.

\section{WikiSRS construction details}
\label{app:wikisrs}

We followed a similar process to \newcite{Pakhomov2010} in selecting the entity pairs to be used in our dataset.
We first filtered the full list of Wikipedia pages to the subset that we learned embeddings for, and then used
the entity types assigned to these pages in YAGO \cite{YAGO3} to restrict to only entities labeled with WordNet
types organization or person, or with the YAGO type geoEntity.  For each pairing of these categories
(Organization-Organization, Organization-Place, Organization-Person, Place-Place, Place-Person, and Person-Person),
we manually selected 30 pairs of entities for each of the following relatedness categories: Completely Unrelated,
Somewhat Unrelated, Somewhat Related, and Highly Related.  These produced the list of 720 entity pairs we used
for our Mechanical Turk surveys.

We augmented each survey of 30 questions with 4 manually-created validation pairs using common entities (e.g.,
London, New York), each of which was categorized as Highly Related or Completely Unrelated.  We included these
validation questions at random indices in our surveys.  To evaluate if participants were reading the questions,
we binned their ratings on these validation questions into 0-25 (Completely Unrelated), 26-50 (Somewhat Unrelated),
51-75 (Somewhat Related), and 76-100 (Highly Related).  If a participant's ratings disagreed with ours on multiple
validation questions, we discarded their data (we allowed disagreement on a single question, as some validation
questions had high variance in responses among reliable annotators).

We recruited 6 participants for each survey, for a total of 34 unique participants across the 48 HITs.  Participants
were presented with a message describing the survey and stating that by clicking the button at the bottom of the message
to begin the survey, they were providing informed consent to participate.  Identifying participant data was not collected,
and we used only the anonymous worker IDs provided by the Mechanical Turk interface to collate our data and remunerate
workers.  Participants were asked optional demographic questions about their age bracket and native language at the end
of the survey; we did not end up using age information, but filtered our participants for those that self-reported English
reading proficiency.  The majority responded to a single HIT, while 3 completed more than 20.  We discarded all submissions
from 3 participants, as they did not report English reading proficiency (1) or did not satisfy the validation questions (2).
All participants were paid state minimum wage at the time of the study for their time, regardless of whether they answered
demographic questions or if we used their data in the final sample.  Collection of this data was approved under Ohio State
University IRB protocol 2017E0050.

To generate the final dataset, we assessed each participant's responses to the validation questions in each survey.  We kept
surveys for which we had at least 4 participants with satisfactory answers to the validation questions; this resulted in
discarding 1 of the 24 HITs for each task.  Due to 2 repeated pairs, this gave us final dataset sizes of 688 pairs for each
of similarity and relatedness, 658 of which were shared between the tasks.

\input{tbls/tbl-wikisrs-icc}

Following \newcite{Pakhomov2010}, we assessed inter-annotator agreement using the intraclass correlation coefficient (ICC).
Table~\ref{tbl:wikisrs-icc} gives the values for our datasets. The numbers reported are within the moderate range, and they
correspond to the ICC numbers reported by Pakhomov et al.\ on the UMNSRS datasets.

The source code of our Mechanical Turk interface and data files used to generate the tasks are available
at {\tt github.com/OSU-slatelab/WikiSRS}.

\section*{Acknowledgments}

We would like to thank Chaitanya Shivade for helpful discussions, and all
of our anonymous reviewers for their invaluable advice.
This research was supported in part by the Intramural Research Program of the
National Institutes of Health, Clinical Research Center and through an
Inter-Agency Agreement with the US Social Security Administration.

redpajama_set_name: RedPajamaArXiv


So what is it like - convention talk - Furtopia | Family Friendly Furry Forum and IRC Chat!
I doubt I'll be able to go to any convention anytime soon, but I have a few burning questions.
How easy or difficult is it to start talking with a random person you never met before and might never see again whom you know nothing about?
Is there anyone else out there who was like me but was able to open up at a con? Stories hopefully?
How easy is it to find others who are willing to take in a guy like me who essentially doesn't talk, doesn't know meanings of some stuff, and automatically tries to hide?
Why are you afraid to meet/talk to other people? There has to be a reason. Some sort of "focal point" in your life that made you the way you are? Once you find it, understand it, and/or get help for it, then maybe you can have a happier life and start interacting with people more.
Conventions don't have to be scary. They may seem like it because they are so big and there's so much to do and see, and there's so many people around whom you do not know. But once you find a furry friend or two to get to know and converse with, I'm sure they'd be more than willing and helpful to show you around the convention site and participate in con events with you.
Making friends isn't easy, but it's not impossible either. Best way I've found to make friends is starting out with "small talk". Perhaps you see someone who is drawing furry art and you like what they are doing. Don't be afraid to go up to them, say Hi, and compliment them on their art. After that, maybe exchange names, discuss art styles and techniques, colors, etc.
Point is, once you "get your foot in the door" so to speak and start making small talk, then more "bigger" conversation will eventually follow.
This depends on the convention. At a big con, like MFF or Anthrocon there are a lot more people, but they are usually already socializing in small groups with their own friends. You'll probably have the best luck by joining in on organized activities (board games, panels, etc), then trying to talk to other people who aren't already in a closed social group.
At smaller cons, I've noticed that people are a lot more willing to strike up random conversations if you just hang out near tables, food, or general points of interest in the convention area. However, the most difficult part is actually making a connection beyond casual smalltalk. Either way, try to ask people questions about themselves that let them share something personal. For example, people *love* to talk in great detail about their fursonas at a furry convention!
When you're done talking, you really need to exchange contact info or plan to meet later, especially at big cons. I still find this whole process to be much more difficult than making friends online, but once you get each other's contact you can move it into that space.
Myself. I went to MFF last year, and I had a lot of trouble finding out how to talk to people; everyone seemed like they were just hanging out with their friend-groups and I was not sure how to approach. So, I decided to challenge myself to take photos with as many vulpine fursuiters as I could. That helped me talk to people in the lobby. However, the best solution turned out to be the game room. I enjoy playing Magic the Gathering, so I brought some cards with me. Pretty soon, I was meeting other furries who wanted to hang out and talk whilst playing MTG. Honestly, most of the time we didn't even play much, just talked.
There are definitely people who are willing; the hardest part is getting through the initial challenges of establishing a friendship.
My first furry con experience was Further Confusion 2016 in January. I didn't know anyone. I was just starting to become active in furry fandom. I didn't know anyone there, but I wandered around, went to panels and a photoshoot for the studio which is building my fursuit. I did ask questions and make comments at one panel, and chatted briefly with a few con goers, but mostly I was a spectator. I'm not an outgoing person at first; I like to observe and then begin to participate.
Jump forward about 6 weeks to the first time I met some of the local fur group in person at the monthly furbowl. I'd already connected online with a few members of the group. I set foot into the bowling alley and was quickly greeted by several furs. Conversation quickly developed, and within fifteen minutes I knew I was among friends. In less than two months I've become a dedicated member of the fur group and am reaching out to say "Hi! Welcome!" to newcomers--just as the fandom has done for me.
Establishing connections at a huge con can be daunting. It was hard for me--a 55+ greymuzzle dipping a paw into active fandom for the first time. Instead, I made my initial connections among the 25 furs or so at the local furbowl. Those connections have led to more contacts outside of my immediate area. It's happened amazingly fast.


text:
Q: Execute Stored Procedure from a Function I know this has been asked to death, and I know why SQL Server doesn't let you do it.
But is there any workaround for this, other than using Extended Stored Procedures?
And please don't tell me to convert my function into a procedure...
So what I'm really asking is: Is there ANY way to run a Stored Procedure from within a Function?
EDIT:
Point proven: there is a way around it, but it's so WRONG I wouldn't do it.
I'm gonna change it to a Stored Procedure and execute it elsewhere.

A: EDIT: I haven't tried this, so I can't vouch for it! And you already know you shouldn't be doing this, so please don't do it. BUT...
Try looking here: http://sqlblog.com/blogs/denis_gobo/archive/2008/05/08/6703.aspx
The key bit is this bit which I have attempted to tweak for your purposes:
DECLARE @SQL varchar(500)

SELECT @SQL = 'osql -S' +@@servername +' -E -q "exec dbName..sprocName "'

EXEC master..xp_cmdshell @SQL

A: Functions are not allowed to have side-effects such as altering table contents.
Stored Procedures are.
If a function called a stored procedure, the function would become able to have side-effects.

So, sorry, but no, you can't call a stored procedure from a function.

A: Another option, in addition to using OPENQUERY and xp_cmdshell, is to use SQLCLR (SQL Server's "CLR Integration" feature). Not only is the SQLCLR option more secure than those other two methods, but there is also the potential benefit of being able to call the stored procedure in the current session such that it would have access to any session-based objects or settings, such as:

*

*temporary tables

*temporary stored procedures

*CONTEXT_INFO

This can be achieved by using "context connection = true;" as the ConnectionString. Just keep in mind that all other restrictions placed on T-SQL User-Defined Functions will be enforced (i.e. cannot have any side-effects).
If you use a regular connection (i.e. not using the context connection), then it will operate as an independent call, just like it does when using the OPENQUERY and xp_cmdshell methods.
HOWEVER, please keep in mind that if you will be using a function that calls a stored procedure (regardless of which of the 3 noted methods you use) in a statement that affects more than 1 row, then the behavior cannot be expected to run once per row. As @MartinSmith mentioned in a comment on @MatBailie's answer, the Query Optimizer does not guarantee either the timing or number of executions of functions. But if you are using it in a SET @Variable = function(); statement or SELECT * FROM function(); query, then it should be ok.
An example of using a .NET / C# SQLCLR user-defined function to execute a stored procedure is shown in the following article (which I wrote):
Stairway to SQLCLR Level 2: Sample Stored Procedure and Function

A: Here is another possible workaround:
if exists (select * from master..sysservers where srvname = 'loopback')
    exec sp_dropserver 'loopback'
go
exec sp_addlinkedserver @server = N'loopback', @srvproduct = N'', @provider = N'SQLOLEDB', @datasrc = @@servername
go

create function testit()
    returns int
as
begin
    declare @res int;
    select @res=count(*) from openquery(loopback, 'exec sp_who');
    return @res
end
go

select dbo.testit()

It's not so scary as xp_cmdshell but also has too many implications for practical use.

A: I have figured out a solution to this problem. We can build a Function or View with "rendered" sql in a stored procedure that can then be executed as normal.
1.Create another sproc
CREATE PROCEDURE [dbo].[usp_FunctionBuilder]
DECLARE @outerSql VARCHAR(MAX)
DECLARE @innerSql VARCHAR(MAX)

2.Build the dynamic sql that you want to execute in your function (Example: you could use a loop and union, you could read in another sproc, use if statements and parameters for conditional sql, etc.)
SET @innerSql = 'your sql'

3.Wrap the @innerSql in a create function statement and define any external parameters that you have used in the @innerSql so they can be passed into the generated function.
SET @outerSql = 'CREATE FUNCTION [dbo].[fn_GeneratedFunction] ( @Param varchar(10))
RETURNS TABLE
AS
RETURN
' + @innerSql;

EXEC(@outerSql)

This is just pseudocode but the solution solves many problems such as linked server limitations, parameters, dynamic sql in function, dynamic server/database/table name, loops, etc.
You will need to tweak it to your needs, (Example: changing the return in the function)

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Purdue University Global's Master of Science in Psychology with a concentration in Applied Behavior Analysis is designed for students who are looking for a program that applies modern psychological theories to special populations. Find out more about this online master's degree program here.
Students considering the Master of Science in Psychology program with a concentration in Applied Behavior Analysis can benefit by having some psychology knowledge or undergraduate education and should be interested in a career involving psychology and behavior. All applicants to Purdue University Global's graduate programs should already have a bachelor's degree; some programs may have additional requirements.
Purdue University Global's Master of Science in Psychology program with a concentration in Applied Behavior Analysis was designed to provide students with a foundation of basic psychology principles and theories as well as present students the opportunity to gain knowledge about special populations. Classes are intended to develop student understanding of special populations, such as the mentally disabled, the autistic, and those with traumatic brain injuries, as well as provide the opportunity for students to apply behavioral concepts and develop assessment and intervention techniques. Master of Science in Psychology students can choose between a practicum or nonpracticum route to complete their degree.
65 or 70 credits depending on if you choose the Nonpracticum or Practicum route, respectively.
The Master of Science in Psychology program with a concentration in Applied Behavior Analysis has two different track options. Student who choose the nonpracticum track will complete 65 credits, which includes 40 credits of core courses and 25 credits of electives. The practicum exam track requires 70 credits, which are made up of 40 core credits and 30 elective credits. Contact Purdue University Global to confirm the most accurate information before enrolling in a program.


text:
A Glasgow Society of Lady Artists (em português: Sociedade das Mulheres Artistas de Glasgow) foi fundada em 1882 por oito alunas da Escola de Arte de Glasgow com o objetivo de proporcionar o devido reconhecimento às mulheres no campo da arte. A Sociedade foi descrita por Jude Burkhauser como sendo "o primeiro clube residencial na Escócia dirigido por e para mulheres". No início, elas se reuniam na Rua Wellington, n. 136, Glasgow.

Não se sabe exatamente quem foram os membros fundadores do clube, mas acredita-se que, entre elas, estavam: a primeira presidente, Georgina Mossman Greenlees, Sra. Joseph Agnew, Elizabeth Patrick, Margaret M. Campbell, Henrietta Robertson, a tesoureira Frieda Rohl, Jane Nisbet, Helen Salmon, Jane Cowan Wyper, Margaret Macdonald (não Margaret Macdonald Mackintosh), Isabella Ure e Sra. Provan. Todas elas eram membros da Escola de Arte de Glasgow, além de artistas, professoras e trabalhadoras artísticas de sucesso. Seu primeiro encontro, em 1882, foi realizado no estúdio de Robert Greenlees, pai de Georgina Greenlees, quem ajudou o grupo a escrever um livro de normas. O objetivo da Sociedade era "O estudo da Arte, a ser promovido por meio de aulas de vida e encontros mensais durante os quais os sócios serão obrigados a exibir esboços, e por uma exposição anual de obras dos sócios".

Em 1895, o grupo havia acumulado fundos suficientes para permitir a compra de uma casa no n. 5 da Blythswood Square. Em 1897, George Henry Walton e Fred Rowntree, em parceria, projetaram e construíram uma galeria para a 14ª Exposição Anual do Clube. Em 1898, um incêndio no edifício do clube destruiu os registros mais antigos da Sociedade.

Outro incêndio desastroso em 27 de maio de 1901 destruiu a galeria e as imagens de uma exposição especial de verão, montada em conjunto com a Exposição Internacional no Museu e Galeria de Arte de Kelvingrove. A galeria foi reconstruída com um projeto de George Henry Walton, e a primeira exposição foi realizada em 25 de outubro de 1902.

Em 1907, um Comitê de Decoração encomendou a Charles Rennie Mackintosh a execução de alguns trabalhos internos e da impressionante porta neo-clássica com frontão preto. O clube prosperou pelos 64 anos seguintes, até 1971, quando foi vendido ao Scottish Arts Council.

Alguns membros estavam determinados a reviver a Sociedade, o que eventualmente aconteceu em 1975, quando ela foi renomeada como The Glasgow Society of Women Artists, com uma Exposição do Centenário sendo realizada na Galeria Collins em 1982.

Membros notáveis

 Janet Aitken
 Katharine Cameron
 Stansmore Dean Stevenson
 de Courcy Lewthwaite Dewar
 Georgina Greenlees
 Jessie Keppie
 Jessie M. King
 Ann Macbeth
 Jessie Newbery

1882 na arte
Organizações fundadas em 1882
Organizações da Escócia
Sociedades artísticas

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Pullach, Germany – 6. November 2018: IFCO, the leading global provider of reusable packaging solutions for fresh foods, announced today the results of a study on the impact of IFCO reusable plastic containers (RPCs) on the shelf life of fresh produce.IFCO RPCs extend the freshness and visual appeal of fresh fruits and vegetables for up to four days compared to single-use packaging.
The study was commissioned by IFCO and conducted by Dr. Lippert Quality Management. The firm conducted realistic tests on four common products – melons, tomatoes, mushrooms and grapes – across the entire supply chain. The results show that IFCO RPCs help retailers increase profits by significantly reducing spoilage and waste, while boosting consumer satisfaction and loyalty. The RPCs already make a significant contribution to helping retailers achieve their sustainability and environmental goals and enabling them to prolong the shelf life of fresh produce extends these benefits even further.


Which web filtering inspection mode inspects DNS traffic?
A. The allowed actions for URL filtering include allow, block, monitor and exempt.
B. The allowed actions for URL filtering are Allow and Block only.
C. URL filters may be based on patterns using simple text, wildcards and regular expressions.
D. URL filters are based on simple text only and require an exact match.
Which of the following regular expression patterns make the terms "confidential data" case insensitive?
A. It is based on the IPS engine.
B. It is based on the AV engine.
C. It can be applied to SSL encrypted traffic.
D. Application control cannot be applied to SSL encrypted traffic.
How do you configure a FortiGate to apply traffic shaping to P2P traffic, such as BitTorrent?
A. Apply a traffic shaper to a BitTorrent entry in an application control list, which is then applied to a firewall policy.
B. Enable the shape option in a firewall policy with service set to BitTorrent.
C. Define a DLP rule to match against BitTorrent traffic and include the rule in a DLP sensor with traffic shaping enabled.
D. Apply a traffic shaper to a protocol options profile.
A. Shared traffic shaping cannot be used.
B. Only traffic matching the application control signature is shaped.
C. Can limit the bandwidth usage of heavy traffic applications.
D. Per-IP traffic shaping cannot be used.
Which of the following conditions is NOT required for this static default route to be displayed in the FortiGate unit's routing table?
A. The Administrative Status of the wan1 interface is displayed as Up.
B. The Link Status of the wan1 interface is displayed as Up.
C. All other default routes should have an equal or higher distance.
D. You must disable DHCP client on that interface.
When does a FortiGate load-share traffic between two static routes to the same destination subnet?
A. When they have the same cost and distance.
B. When they have the same distance and the same weight.
C. When they have the same distance and different priority.
D. When they have the same distance and same priority.
Examine the static route configuration shown below; then answer the question following it.
A. All traffic to 172.20.1.0/24 is dropped by the FortiGate.
B. As long as port1 is up, all traffic to 172.20.1.0/24 is routed by the static route number 1. If the interface port1 is down, the traffic is routed using the blackhole route.
C. The FortiGate unit does NOT create a session entry in the session table when the traffic is being routed by the blackhole route.
D. The FortiGate unit creates a session entry in the session table when the traffic is being routed by the blackhole route.


How To Find Happiness in Your Life?
All of us strive to be happy and satisfied with our lives. They want to be happy with every aspect of their life, from love to career, and of course, life in general. But how is it possible to get to the point of that type of happiness?
Many people claim to be extremely happy but it is clear that it isn't true. It is possible to achieve a state of extreme happiness but just how to reach it? Here are a few ways that will make you a lot happier.
We all love the odd 'naughty' bit of food and having the occasional night in, laid on the sofa watching trashy TV shows or movies. However, it cannot be denied just how good we feel when we are actually trying to be healthier. Exercising releases endorphins which make us feel great and eating just a little bit cleaner always makes us feel much better, both inside and out. Just making a few small changes to our lifestyle to be a little healthier and fitter can do wonders for both our physical and mental health.
Not only that but working on your health does wonders for other areas of your life too. You will find you have much more energy to do the interesting things in life and won't be struggling as much to achieve them. It can do wonders for your dating life too – you're less likely to feel guilty about the odd meal that will affect your waistline – if you are making a conscious effort to be healthier, the odd naughty meal shouldn't affect at all and you will be able to endure a nice, guilt free dinner date with a partner of your choosing.
Life can sometimes be tough but the support of a good partner is always good for keeping our happiness levels up. Sometimes our love life needs a bit of work and that's okay – it is important to talk things through with our partners and finding that common middle ground. Discussing problems as adults will always help to make your relationship stronger and better.
Some people may be single and looking for love in order to make them happier – we all need that person to lean on, after all. Some people try online dating sites to find like-minded people and go out and have a good time. It will definitely help put a smile on your face! Focus on finding people who have similar interests and that problem of your love life being virtually nonexistent will soon be no more and you can get out and enjoy yourself with a partner once again.
It is so important to work on your relationship and improve it if it needs the work though – people often find that they give up too easily when it comes to such problems and this doesn't do anything to help. In fact, it makes everyone even more unhappy. Happiness in a relationship should be felt by both parties so in order to improve your love life, you both need to put the effort in and discuss problems as they arise.
Whether you are single and turning towards friends and family or whether you are in a relationship and leaning on their partner for support, people always require someone to listen to them – it can be frustrating to not have anyone to talk to when you really need it.
Just knowing that someone is there to help take the stresses of the day away can sometimes make all the difference.
Happiness is a state of mind and anything that helps to improve this state of mind is a plus. Everyone has their own little things that make them happy – whether it be a hug from a friend or simply remembering to record their favorite TV show. If these little things get done, it shows them someone cares and they can be secure and happy in that fact.
They will vary from person to person but knowing what you like will help to put a smile on. If someone makes the effort to do that thing for you, it definitely proves just how much they care.


Excessive exposure to UVB rays can cause a degradation of Hyaluronic Acid in the skin. Hyaluronic Acid, a form of natural complex sugar, is a major component of the connective tissue matrix in the dermis. It contributes significantly to cell proliferation and migration, playing a very important role in the way your skin functions, looks and feels.
A high concentration of MHA also helps to attract and retain water within the extracellular space, hydrating the skin and increasing its volume and density. It also helps with the transportation of essential nutrients to the skin's viable cells, which is a major component in tissue repair.
The Multi Hyaluronic Ampoule will penetrate into the skin's surface and replenish the natural level of hyaluronic acid, at the same time combating signs of aging such as wrinkles and fine lines.


The College Board is in hot water. After administering an easy June SAT with a brutal curve, critics came out of the woodwork to voice their discontent, and tens of thousands of students signed an online petition demanding that their tests be rescored. June was not the first time the SAT administered an easier test that resulted in a harder curve – occasional variation of this nature does occur from time to time, and I initially attributed the June scoring debacle to such random variation. I've since come to understand that the scoring issues on the June SAT were not a reflection of natural variation, but resulted from specific decisions made by the College Board. The wound was essentially self-inflicted, and the College Board is at risk of repeating its errors on subsequent SATs. Will the College Board be able address the underlying issues before it administers another problematic test in August?
As it undertook the process of designing and writing the new SAT, the College Board decided to distance itself from its historic partner, the Educational Testing Service (ETS). The ETS, a non-profit organization, has for decades designed SATs, GREs, TOEFL tests and many other assessments. With the dawning of the Redesigned SAT, the College Board shifted the ETS from its test creation role to a consulting and test administration role, and the College Board took responsibility for generating and pre-testing questions.
To shorten the length of the SAT, putting it in line with the ACT, the College Board decided to retire its time-honored experimental section. For decades the College Board pre-tested items using experimental testing sections that appeared on every official SAT. Students across the country knew that one of their ten test sections would not count towards their score, but would help build future SATs.
Because students could not be certain which of the SAT test sections was experimental, they approached every section as if it counted. This provided a robust national sample of students to pre-test questions to gauge levels of difficulty and to ensure that questions functioned in a similar way for all students, without bias. When the SAT dropped the experimental section, it lost an effective means of pre-testing questions using a nationally representative sample of students.
To compensate for the loss of its experimental section, the College Board decided to pre-test questions on a subset of students who had opted out of the SAT essay. This decision was criticized, as pundits questioned whether the sample of pre-tested students would be representative and whether the data gathered would be robust. How seriously would students treat this new section, knowing that it would have zero bearing on their test scores? Would a subset of students who elected not to take the SAT's optional essay be representative of the full national sample of students?
Although there are statistical methods to identify students who did not take the questions seriously and remove their data from the sample, the approach raises concerns. Would the students who took this test section seriously provide enough data to prepare questions for a national sample?
Students applying to the most selective colleges would be much more likely to take the SAT essay and avoid the experimental section. The College Board reported that 70% of SAT takers took the essay in 2017, and it's safe to say that students shooting for the most selective schools in the nation were included in that 70%. Until recently, schools such as Harvard, Yale, Duke, Stanford, Princeton and others required the SAT essay for admission. Students applying to those top schools would naturally take the SAT essay and would never be exposed to the experimental SAT sections. Losing this group of students for the pre-testing process was a major factor in the College Board creating a test that was too easy.
Another issue may have affected the sampling strategy. Within the limited sample of students who received the experimental section, a higher proportion of male students may have dismissed the section.. Given the research on male and female student behavior in academia, this would not be remotely surprising. Losing more male students in the pretesting process would be problematic, especially for assessing the relative difficulty of math items. Male students are overrepresented at the top of the SAT math scale. In the 2016 Profile Report, the College Board reported that of the 117,067 students with SAT math scores in the 700-800 point range, males comprised 61.5% of the total and females 38.5% of the total. If too many male students ignored the experimental section, the College Board's appraisal of the relatively difficulty of math items might have been compromised.
So the College Board spent several years pre-testing questions on a sample of students that was not representative of the broader population of test takers. It administered numerous SATs without incident between March 2016 and May 2018, but the June 2018 SAT revealed a fundamental weakness in the pre-testing strategy. The difficulty level of questions was established using a sample of students that did not include the strongest test takers. When students across the country took the June SAT, the pre-tested "hard" items did not function as expected, and too many students answered too many problems correctly. This resulted in a very steep scoring curve and a wave of student protests and widespread criticism. The challenge for the College Board is that the August SAT has been pre-tested in a similar fashion, and has the same potential weakness in terms of measured difficulty level. The College Board is aware of this risk and is working to ensure the scoring fiasco does not repeat.
Meanwhile, the ACT, Inc. just announced that it is adding an experimental section to the ACT, to be required by all students on every test. Pre-testing is serious business. Experimental sections have been a staple of standardized tests for years, and for good reason. If test writers don't properly determine how test questions function, downstream scoring issues like the one we saw in June can occur with greater frequency. Will the College Board fix the August test in time? Most likely yes. There's too much at stake to miss again. And we'll know within a matter of weeks.


Attack Method: Coiling and crushing their prey.
Lifespan: Unknown, thought to live indefinitely.
From far away, they appear to be an extremely large snake, but with closer inspection, fungal snakes are a mass colony of mushrooms and molds forming together to make a large, slithering body. These colonies are tightly rooted together, tightening and contracting to simulate muscle so that it may have mobility. The head of the snake is the main control system of the entire colony, if one were to behead it the entire body will fall apart, however attempting to decapitate a fungal snake is a feat that not even some of the sharpest tools can accomplish due to the high density of mushrooms that make up it's flesh.
Fungal snakes may have a mouth or lack one all together, however it has no purpose beyond snaring prey and lacks teeth or a digestive system. They may possess from 1 to 8 pseudo eyes all along their bodies; while their eyesight is extremely poor they are able to pick up on bright lights and colors. Fungal snakes normally come in shades of greys and washed out colors, however brightly colored snakes have been bred in captivity.
Starting as just single spores, it takes years for a full grown fungal snake to form together. They're on a constant search for food, its main diet consisting of decaying material. While a scavenger at their core, fungal snakes do seek prey, trapping anyone that gets too close to their domain and leaving the body to be consumed when deemed 'ripe' enough for consumption. Fungal snakes thrive in humid and damp environments, but hunger may drive them into more urban territories so they may feed on garbage as a last resort.
Incredibly territorial, upon meeting another of their kind they will coil around each other tightly, trying to assimilate one another into their colonies. This fight can last from days to months of the two violently thrashing and squeezing the other until one gives up, and allows the other snake to pull it into its mass.
• It is said that fungal snakes started to spread to other planets on spores carried by the ships that brought the Crawlerz into the space age. These spores are microscopic, and almost impossible to detect. While harmless to most living species, these spores can float around for eons, even surviving in space before landing in the perfect conditions to grow a new fungal snake.


text:
HOME WOMEN'S CYCLING INTERVIEWS CALENDAR TEAMS & RIDERS LATEST NEWS RSS CONTACT
Astana-Premier Tech re-signs seven riders for 2021
Nov 24 2020 07:50 am CET
Astana Pro Team has announced the contract extensions of seven riders for the 2021 season.
The team -which will be called Astana-Premier Tech from January 2021- has confirmed the extensions for Manuele Boaro, Davide Martinelli, Jonas Gregaard Wilsly, Rodrigo Contreras, Luis León Sánchez, Ion and Gorka Izagirre for the upcoming season.
Ion Izagirre, stage winner of all three Grand Tours, is pleased to have another year in the formation along with his brother. "Together with my brother I joined the team in 2019," the Spaniard said. "It has been great two years, therefore, I am happy to re-sign for another season.
"This year had ups and downs, to leave the Tour de France was shattering but winning a stage at the Vuelta a España was a happy ending to a challenging season. I am looking forward to 2021 and also to stay in the team with my brother. As always, we have big ambitions and are already in full swing to start the season in good shape."
Spanish National champion Sanchez welcomed the news of his one-year extension. "I am glad to continue with the team, I feel very comfortable here, as it is a familiar team with a professional surrounding," he explained. "Also, I know my role within the team, even so I get my chances to show myself. I am already many years in the team, since 2015, but my motivation and ambitions remain the same.
"With my experience I think, I can take the young riders by the hand but will also help our GC leaders to fight for the best results possible. After a difficult 2020 season I am looking forward to 2021, hopefully being again a normal cycling season for us."
After this abnormal season -which was his first in the Kazakh-Canadian team- Martinelli is glad that he gets another year to enjoy. "I am very happy because after a very strange and difficult season due to Covid-19, I hope that we can have a regular 2021 season without any problems," the Italian commented.
"I got the chance to continue working with the team, where I want to show, especially in the Spring Classics, my potential. For these goals, I am working already now on it to be in the best shape. I really want to thank the team and the management for believing in me, I will give my best to pay off their confidence in me."
MANUELE BOARO
DAVIDE MARTINELLI
JONAS GREGAARD WILSLY
RODRIGO CONTRERAS PINZON
LUIS LEON SANCHEZ GIL
GORKA IZAGIRRE INSAUSTI
ION IZAGIRRE INSAUSTI
Tour du Rwanda postpones 2021 edition of the race due to Covid-19
Vuelta a San Juan organisation suspends 2021 edition of the race
Wout Van Aert takes fourth Belgian national cyclocross title
Mikel Landa to focus on Grand Tours as Bahrain Victorious outlines objectives for 2021
Marc Hirschi chooses new team and signs with UAE Team Emirates for three years
Riders and staff of UAE Team Emirates receive Covid-19 vaccine
Team DSM and Marc Hirschi announce a surprising break-up
Lotto Soudal Ladies welcomes Digitalum as new partner
Israel Start-Up Nation to train in Spain without Chris Froome
Patrick Lefevere on 2021 season: We start with the same enthusiasm, ambition and determination
CyclingPub says farewell to 2020 and welcomes 2021 with renewed optimism
Sander Armée and Mauro Schmid join Qhubeka Assos in 2021
1. Tour du Rwanda postpones 2021 edition of the race due to Covid-19
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THE LATEST CYCLING NEWS IN YOUR MAILBOX
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text:
Internet Scofflaw
Thoughts on politics, computer science, philosophy, the media, and whatever else strikes my fancy.
Obama anti-gun orders based on lies
Yesterday, the White House announced the president's latest anti-gun executive orders:
One measure would close a loophole under which felons, those convicted of domestic violence and others banned from having guns can evade required background checks for machine guns or other weapons by registering the gun to a trust or corporation. The ATF received more than 39,000 requests last year for transfers of restricted firearms to trusts or corporations, Biden said, calling it "a very artful dodge to get around people who are not capable legally of owning weapons to be able to gain access."
The White House adds that "felons, domestic abusers, and others prohibited from having guns can easily evade the required background check . . . by registering the weapon to a trust or corporation."
Not so, explains John Lott:
Yes, when registered to a corporation any officer is allowed to posses the machine gun, but the point that the transfer occurs still requires a NICS check for the person actually picking up the gun.
So registering a gun to a corporation does not allow you to evade the background check. That is a lie.
The effect of this rule is that a corporation cannot register a weapon unless all of its officers are permitted to possess it, even those officers who never will. One domestic abuse charge is enough to blackball an entire company.
And why do companies need to register weapons anyway? Because the government has made them prohibitively expensive for individuals.
The second order is also dishonest:
The other measure will end a government practice that allows military weapons, sold or donated by the United States to allies, to be re-imported into the United States by private groups. The White House said the United States has approved the re-importation of 250,000 such guns since 2005; under the new policy, only museums and a few other groups such as the government will be allowed to re-import these weapons.
The weapon at issue here, what the White House calls "military grade", is the obsolete M-1 Garand, which the United States produced in enormous quantities during World War 2. Lott explains that the M-1 is just like any civilian .30-06 hunting rifle, except that it is too heavy by modern standards. It is "military grade" in the exact same sense as a 17th-century blunderbuss: it was once used by the military. Today it is mainly sought after by collectors.
POSTSCRIPT: The media, alas, has been typically credulous in reporting the president's anti-gun orders, taking the White House's claims at face value. For example, NPR simply quoted the White House "fact sheet", without doing the minimal reporting that would have shown that it wasn't true. The Washington Post did note the bogosity of the reimportation rule, but not the registration rule.
This entry was posted on Friday, August 30th, 2013 at 12:28 pm and is filed under Media Failure, Political. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.
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