Ferdinand Parpan has become a highly celebrated European sculptor, and this work "Femme" from 1964 is a typically beautiful example of his uniquely stylized figuration. Having attended the Place des Voges in the 1920's, Parpan started working as an engraver, but in the 1930's inspired by the Art Deco sculptor Dardé, he began carving in wood, and stone. He soon had his first exhibition on Avenue Victor-Hugo in 1936, and was encouraged by the sale of several pieces. But his progress was interrupted when he was called up to fight in the war. Resuming his passion after the Liberation in 1944, Parpan took a studio in rue du Retrait in Paris with the sculptor Louis Roy. Whilst undertaking several public commissions to replace works that had been destroyed during the war, he became intrigued by the aesthetics of religious art and the veneration it evoked, and decided to adapt certain aspects into his own work to parallel the Modernist reverence of purity of form. This development defined the future of his oeuvre and received universal acclaim. It is this characteristic elegance and refinement of form that so successfully defines this magnificent work "Femme".
From 1947 Parpan exhibited widely in the Paris salons such as the Salon des Indépendants, Salon d'Automne, Salon de la Jeune Sculpture, Salon d'Asnieres, Salon de L'Art Libre, Salon des Artistes Francais. In 1950 he was appointed chairman of Salon des Indépendants. That same year, a major solo exhibition of his work was held at the Grande Galerie, on rue Faubourg Saint-Honoré, which firmly established his name.
The artist went on to enjoy an international career, notable exhibitions include 1954 at Sarrebruck, Germany; 1956 Metz, France; 1957 Strasbourg; 1958 Galerie Bernheim, Paris; 1964 Galerie Henquez; 1970 Musée Rodin, Paris; 1987 Fine Art Society, Los Angeles; 1990 Ato Gallery,Tokyo; 1995 Soufer Gallery, New York. He has also been honoured with several major awards including in 1965 the Prix d'Honneur de la Sculpture; 1972 Médaillé de la Ville de Paris; 1991 Grand Prix d'Européen de la Sculpture. In 2000 a permanent exhibition of his work was opened at the Musée de Vaz-Obervaz, in Switzerland.
The artist's work is represented in various museums of Modern Art including the Musée d'Art Moderne, Paris.


In a bowl combine the 1/2 cup of cane sugar and freshly sliced strawberries, stirring them together and setting aside for an hour. Drain any liquid that pools up and set aside for use later. Preheat the oven to 375 degrees Fahrenheit and grease or butter a muffin pan. You can skip this step if you have paper muffin cups. In a separate large bowl mix the softened canna butter and 1/4 cup cane sugar, subsequently mixing in the eggs one at a time, followed by the vanilla extract. Next, mix in the flour, salt, nutmeg, baking soda and juice from the berries. Now stir in the strawberries. Once mixed, you may spoon the batter into the muffin pan and place in the preheated oven to bake for 23 - 27 minutes or until golden brown and a toothpick comes out of the center clean.
The picture for these makes them look so good! I want to try them, I have had blueberry muffins with cannabis before but never strawberry.


text:
Marshall McDonald Jazz Project
On Saturday, July 6, Westchester Collaborative Theater (WCT)
continues its Jazz Masters music series with the Marshall McDonald Jazz Project, McDonald, a veteran of the New York jazz scene, and his band will be appearing for two sets at 7:30 and 9 pm. Joining Marshall will be David Childs on piano, Nori Naraoka on bass And George Coleman Jr. on drums.
Brand New Shorts
Fridays at 8pm, Saturdays at 2 & 8pm, and Sundays at 3pm
July 19th - 28th
Brand New Shorts, our latest 10-minute play festival directed by Ossining resident Nathan Flower, associate arts professor at NYU's Tisch Drama, will run weekends from Friday, July 19 through Sunday, July 28. An eclectic mix of zany, offbeat comedy and thought-provoking drama, the production will showcase nine never before seen plays by local playwrights, all members of Westchester Collaborative Theater.

redpajama_set_name: RedPajamaCommonCrawl


Please note: Bicycle Network, or an equivalent interstate body is the only body that provides some relevant insurance cover for the Chain Reaction event. Cycling Australia and Triathlon Australia membership insurance is limited to those bodies' sanctioned events only. Chain Reaction is not an event sanctioned by either body.
As a rider in Chain Reaction, you are provided with Chain Reaction branded training kit (jersey, vest, knicks and socks) as well as a Team jersey and vest for the event. Please provide accurate measurements and sizes below.
Please note: The fit is a "European race cut" sizing and therefore we recommend referring to the sizing chart below.
please indicate below which items you DO NOT require.
A group of senior executives who recognise their responsibility to the greater community and believe that individuals working together make a real difference in the lives of sick children and the charities that support them.
A desire to incorporate individual passion for cycling into an annual charity challenge.
Participate in the spirit of camaraderie, teamwork and fun.
Leverage executive standing in the business community to help meet the fundraising objectives.
Contribute where possible to the corporate sponsorship objectives.
Promote and use the Chain Reaction brand responsibly and with the approval of the Organising Committee.
Behave in an exemplary manner while representing Chain Reaction and our supported charities. As Ambassadors for these bodies, any form of discrimination, harassment, bullying, excessive use of alcohol or any form of drug use will not be tolerated or condoned.
That it is not mandatory for each rider to complete the entire distance. Each rider should ride within his/her own ability.
Chain Reaction will be completed as a group, not a race.
Chain Reaction is and should remain a-political.
For insurance purposes, all riders must be financial members of Bicycle Network (or equivalent organisation).
All participants must read and agree to the following indemnity agreement.
1. I acknowledge that I participate in the Chain Reaction Event ('the Event') voluntarily and at my own risk. I understand that participation in the Event or any activity associated with it, including all training activities, involves riding on public roads used by other traffic and I am aware of the hazards in that and in riding bicycles in general.
2. I further acknowledge that participating in the Event may result in a real risk of serious injury or even death from various causes including but not limited to over exertion, dehydration and accidents with other participants, pedestrians, animals or road users and I will take all reasonable measures to protect myself from the risks of participation.
3. I accept responsibility for travel and medical insurance and for payment and/or reimbursement of medical/surgical expenses incurred by me. I consent to receive such medical treatment that may be deemed advisable in the event of my injury, accident and/or illness during training and the Event.
4. In my judgment I have sufficient fitness, competence and experience to participate safely in such riding and to the best of my knowledge I am not suffering from a condition that would prevent and/or render me unfit to participate in the Event. I will immediately notify the Event Organiser in writing of any change to my fitness and ability to participate. I understand and accept that the Event Organiser will continue to rely upon this declaration as evidence of my fitness and ability to participate.
5. I am responsible for all property I bring with me on the Event and understand that the Event Organiser does not accept responsibility for any loss or damage to this property.
6. I undertake to be a financial member of Bicycle Network (or similar organisation) at all times during the training program and the Event.
7. I agree to comply with all the commitments made by me in the Rider Sign-on Form and the rules and directions of Event officials and their servants and/or agents. In particular, I undertake to comply with any direction given or sanction imposed by an Event official or the Event Committee including to withdraw from the Event or from any part of it.
8. I grant the Event Organisers, Chain Reaction Challenge Foundation, its sponsors and its charity partners involved in the Event a royalty free right for use in perpetuity, to use and allow the use of my name, image and likeness in broadcasts, webcast, media press materials, promotional materials and otherwise in connection with the Event.
9. In consideration of and as a condition of acceptance of my participation into this Event I hereby agree to release Chain Reaction Challenge Foundation, Event Organising Committee, Event Organiser, Event Manager, Training Coach, Event Sponsors and each of their agents, affiliates, employees, members, volunteers and any person or body directly or indirectly associated with the Event and waive any right or entitlement I have or, but for my execution hereof, have, or which may accrue, in respect of all and any claims, demands or proceedings arising out of or in connection with my participation in the Event including (without limitation) where such claims, demands or proceedings are caused by their negligence, but excepting wilful negligence, and I indemnify them and will keep them indemnified against all liability for all injury, loss or damage arising out of or in connection with my participation in the Event. This release and indemnity continues forever and binds my heirs, successors, executors, administrators, personal representatives and assigns.
As part of your committment to Chain Reaction and its charity partners, we ask that riders make an upfront donation of $3,000 upon selection to the event. We will require riders to submit their credit card details upon selection into the event and will charge the upfront donation at this time.
We also feel that it is important that our riders understand and agree to the fundraising requirements of this event before completing their submission.
Every rider must raise a minimum of $5,000 in personal donations. Riders are also expected to use their corproate networks to sell sponsorships on the team jerseys. Please note, the corporate sponsorships are not applied to your personal fundraising accounts and are considered "team sponsorships."
Please tick the boxes below that you understand and accept the commitment to fundraising if accepted to the 2019 Chain Reaction Challenge.


If you have issues sleeping without a neck pillow, then the Teal Forest Fetish Neck Pillow from the house of India Circus is an ideal accessory for your air travel. The design features a forest-y composition in a teal background with floral motifs & deer prints, highlighting its jungle vibe. Shop this range by design, price and more.


31-9094 Medical Transcriptionists. … Industries with the highest published employment and wages for this occupation are provided.
What does a Medical Transcriptionist earn in your area? 344 Medical Transcriptionist Salaries … average Medical Transcriptionist salary … Medical Transcriptionists.
Transcriptionist Salary: $30,940(average). … Medical Transcriptionist and Editor. Senior Medical Transcriptionist. Radiology Transcriptionist. Legal Transcriptionist.
The median annual wage for medical transcriptionists was $34,890 in May 2015. Job Outlook.


EMOTIONS are high at the Stanthorpe Scout Den after they were targeted in a break and enter.
Scouts and leaders were left angered and disappointed after they were the victims of a callous break-in, which left a refrigerator beyond repair and several items missing.
One of the items stolen was a speaker that some of the Scouts had built from scratch.
"Our kids had gone away (abseiling in Withcott) for the weekend and we came back on the Sunday to find it'd been broken into," Venturer Scout Leader Jenni Waites said.
"Quite often they'll break in and use our barbecue facilities and come up and have drinking parties. They smash glass and leave it everywhere.
"This time they broke into the den. I don't think the people who would do this understand the word respect. It impacts on quite a few people when it happens."
Sadly, the group isn't strangers to these occurrences, leaving them with little choice but to install security cameras around the den and grounds.
"It's frustrating because that money has to come from money that would have been spent on the kids.
"It's just really sad. These are really good kids.
"One of the ex-group leaders said that they once had about 10 break-ins within about two months.


text:
Q: SelectItem selection not working with duplicated id Public SelectItem[] test{
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  item = new SelectItem(id,name);

What happen here is when i get duplicated id in selectItem, i was not able to select the data from the dropdown because i think selectItems already considered both duplicated data as one.
Is there any other way to cater this so that i can still select that duplicated id from the dropdown ? Because the only things duplicated here is the id represent for  back-end and whatever data displayed from that duplicated id is actually different in the dropdown
P/s : id is not unique and am using jsf 2.x and pf3.5

redpajama_set_name: RedPajamaStackExchange


In Arthur Miller's 1949 play Death of a Salesman, Willy Loman embodies the common American salesperson of his time. Much has changed since Miller brought this character to life, yet the overly macho way people talk about sales remains the same—and it's time for a change.
The following is adapted from Moving from Models to Mindsets.
Preschool kids ask their parents an average of 100 questions a day. By middle school, they've basically stopped asking questions. Curiosity is a lost superpower.
Almost every conversation is an opportunity to build rapport. To do so requires the salesperson to listen harder for rapport cues.
Storytelling in Sales: Whose story are we telling?
In sales, storytelling is all the rage right now. In fact, from what we've seen, teaching storytelling to salespeople has significantly increased in the last five years. We all know there is power in stories, but did you know that listening to someone share an engaging story releases a chemical called "oxytocin" in your brain? Oxytocin is a chemical that relaxes a person and shows up "when we are trusted or shown a kindness, and it motivates cooperation with others," (Zak, HBR "Why Your Brain Loves Story Telling" 2014). Since trust is critical for sales and storytelling builds trust, it's natural to assume a link between storytelling and generating a sale.
Many training companies begin with a book. A thought leader, a researcher, someone with passion and dedication takes their energy and writes a book. They focus on dysfunctional teams, leadership principles or driving sales performance. So far, so good.
Empathy has been a hot topic. Wherever you go in the learning space—from preschool to the boardroom—teachers and leaders are stressing the importance of empathy. The growing number of millennials in the workforce have changed the emotional make up of our corporate world. They are, rightfully, pushing for greater authenticity, meaning, and transparency from the companies they work with. This renewed focus on empathy, however, falls short of its optimistic intentions.
I met with a newly minted leader and long-time colleague, who is both highly collaborative and highly engaging. He cares about his people in a visceral way. I congratulated him on his promotion while warning him that there was the potential for him to fail due to a blind spot in his approach. I stated that he may fail the eat the oatmeal or like the oatmeal challenge.
Want to tap into your participants' natural passions? We should talk. Book some time with us here.
What differentiates JMReid Group from all the other training companies?
We are learner-centric. They are the star of the show, not the facilitator.
We make learning authentic by understanding your context, culture, and strategy.
We stay current on neuroscience, social science, and psychology to bring you the latest and greatest thinking in the field.


Find out the latest on sports injuries and treatment, as well as injury prevention and safety tips.
Chronic Traumatic Encephalopathy in Athletes–How Serious Is It?
As researchers more about the long-term effects of head injuries, particularly in athletes, studies found that multiple head injuries could result in lasting damage to the brain, and CTE was discovered. Perhaps you've heard about chronic traumatic encephalopathy, often referred to as CTE, in news articles about concussions in popular sports like football and boxing….


Connie Fleming is a licensed veterinary technician. She oversees quality control in the laboratory and assists in interpreting cytology slides, blood smears, and urinalyses. Frequently assisting in surgery, she is a very skilled phlebotomist ("she can hit a vein") and is a valued member of our medical team.
Amanda Nation began working as a receptionist, moved to veterinary assistant to further her interest in working "directly with patients", and recently passed her state exam to become a licensed veterinary technician. She has become very skilled in the art of medicating patients and performing laboratory tests. Amanda can also help with basic questions asked by concerned pet owners and gives progress reports for hospitalized pets.
Jennifer Mount, a paramedic with 5 years experience, worked as a veterinary assistant for five more years while living in Florida. She has moved back to North Georgia with her Corgi, Brynn and her Great Dane, Katie. Her cats include a Cornish Rex named Affair, a Scottish Fold named Spanky, and a domestic shorthair named Brendon.


With her new installation at Chisenhale Gallery, Lucia Nogueira plays on our all-too-human feelings of vulnerability. She confronts us literally with darkness, emptiness and the probability of an imminent destructive force. The intense pressure suggested by capped ductile gas pipes, rising through a concrete floor, is foiled by a fragile combination of glass and mercury. An extraordinary elegance constitutes the switch whereby a most awful violence might be unleashed.
Lucia Nogueira was born in Goiania, Brazil, in 1950. In 1975 she moved to London and attended the Chelsea School of Art (1976-9) and Central School of Art (1979-80).
This installation coincides with an exhibition of other work by Lucia Nogueira at the Mario Flecha Gallery, London.
Publication: An illustrated catalogue accompanies this exhibition with text by Adrian Searle.
With thanks to British Gas plc North Thames, Anthony Reynolds Gallery and the Cultural Fund of the Brazilian Embassy.


text:
\section{#1}\label{ssec:#2}}
\newcommand{\isubsection}[2]{\subsection{#1}\label{ssec:#2}}
\newcommand{\isubsubsection}[2]{\subsubsection{#1}\label{ssec:#2}}
\newcommand{\secref}[1]{\textsection{~\ref{ssec:#1}}}
\renewcommand{\algorithmicrequire}{\textbf{Input:}}

\aclfinalcopy

\newcommand\BibTeX{B{\sc ib}\TeX}

\title{Edge-Linear First-Order Dependency Parsing with Undirected Minimum Spanning Tree Inference}

\author{Effi Levi$^1$\\
\And Roi Reichart$^2$\\
          $^1$Institute of Computer Science, The Hebrew Univeristy\\
          $^2$Faculty of Industrial Engineering and Management, Technion, IIT\\
          {\tt \{efle$|$arir\}@cs.huji.ac.il~~roiri@ie.technion.ac.il}\\
\And Ari Rappoport$^1$\\
 }

\date{}

\begin{document}

\maketitle

\begin{abstract}

The run time complexity of state-of-the-art inference algorithms in graph-based dependency parsing
is super-linear in the number of input words ($n$). Recently, pruning algorithms for these models
have shown to cut a large portion of the graph edges, with minimal damage to the resulting parse trees.
Solving the inference problem in run time complexity determined solely
by the number of edges ($m$) is hence of obvious importance.

We propose such an inference algorithm for first-order models, which encodes the problem as
a minimum spanning tree (MST) problem in an \textit{undirected graph}.
This allows us to
utilize state-of-the-art undirected MST algorithms whose run time is $O(m)$ at expectation and with
a very high probability.
A directed parse tree is then inferred from the undirected MST
and is subsequently improved with respect to the directed parsing model
through local greedy updates, both steps running in $O(n)$ time.
In experiments with 18 languages, a variant of the first-order MSTParser~\cite{McDonald:05b}
that employs our algorithm performs very similarly to the original parser that
runs an $O(n^2)$ directed MST inference.

\end{abstract}

\isection{Introduction}{sec:intro}

Dependency parsers are major components of a large number of NLP applications.
As application models are applied to constantly growing amounts of data,
efficiency becomes a major consideration.

In graph-based dependency parsing models \cite{Eisner:00,McDonald:05a,McDonald:05b,Carreras:07,Koo:10},
given an $n$ word sentence and a model order $k$, the run time of exact inference is
$O(n^3)$ for $k = 1$ and $O(n^{k+1})$ for $k > 1$ in the projective case~\cite{Eisner:96,McDonald:06}.
In the non-projective case it is $O(n^2)$ for $k=1$ and NP-hard for $k \geq 2$~\cite{McDonald:07}.~\footnote{We refer to parsing
approaches that produce only projective dependency trees as projective parsing and
to approaches that produce all types of dependency trees as non-projective parsing.}
Consequently, a number of approximated parsers have been introduced, utilizing a variety of techniques:
the Eisner algorithm \cite{McDonald:06},
belief propagation \cite{Smith:08}, dual decomposition \cite{Koo:10,Martins:13}
and multi-commodity flows \cite{Martins:09,Martins:11}.
The run time of all these approximations is super-linear in $n$.

Recent pruning algorithms for graph-based dependency
parsing~\cite{Rush:12,Riedel:12,Zhang:12} have shown to cut a very large portion
of the graph edges, with minimal damage to the resulting parse trees.
For example, \newcite{Rush:12} demonstrated that a single
$O(n)$ pass of vine-pruning \cite{Eisner:05} can preserve
$> 98\%$ of the correct edges, while ruling out  $> 86\%$ of all possible edges.
Such results give strong motivation to solving the inference problem
in a run time complexity that is determined solely by the number of edges ($m$).~\footnote{Some pruning
algorithms require initial construction of the full graph, which requires exactly $n(n-1)$ edge weight computations.
Utilizing other techniques, such as length-dictionary pruning, graph construction and pruning can be
jointly performed in $O(n)$ steps.
We therefore do not include initial graph construction and pruning in
our complexity computations.}

In this paper we propose to formulate the inference problem in first-order (arc-factored) dependency parsing
as a minimum spanning tree (MST) problem in \textit{an undirected graph}.
Our formulation allows us to employ state-of-the-art algorithms for the MST problem in undirected
graphs, whose run time depends solely on the number of edges in the graph.
Importantly,  a parser that employs our undirected inference algorithm
can generate all possible trees, projective and non-projective.

Particularly, the undirected MST problem (\secref{MST}) has a randomized
algorithm which is $O(m)$ at expectation and with a very high probability
(\cite{Karger:95}), as well as an $O(m \cdot \alpha(m,n))$ worst-case deterministic
algorithm \cite{Pettie:02}, where $\alpha(m,n)$ is a certain natural inverse of Ackermann's
function \cite{Hazewinkel:01}. As the inverse of Ackermann's function
grows extremely slowly~\footnote{$\alpha(m,n)$ is less
than 5 for any practical input sizes $(m,n)$.} the deterministic algorithm is in
practice linear in $m$ (\secref{state-of-the-art}).
In the rest of the paper we hence refer to the run time of these two algorithms as
{\it practically linear} in the number of edges $m$.

Our algorithm has four steps (\secref{parser}).
First, it encodes the first-order
dependency parsing inference problem as an undirected MST problem,
in up to $O(m)$ time.
Then, it computes the MST of the resulting undirected graph. Next,
it infers a \textit{unique} directed parse tree from the undirected MST.
Finally, the resulting directed tree
is greedily improved with respect to the directed parsing model.
Importantly, the last two steps take $O(n)$ time, which makes the total run time
of our algorithm $O(m)$ at expectation and with very high probability.~\footnote{The output
dependency tree contains exactly $n-1$ edges, therefore $m \geq n-1$, which makes $O(m) + O(n) = O(m)$.}

We integrated our inference algorithm into the first-order parser of~\cite{McDonald:05b}
and compared the resulting parser to the original parser which employs
the Chu-Liu-Edmonds algorithm (CLE, \cite{Chu:65,Edmonds:67}) for inference.
CLE is the most efficient exact inference algorithm for graph-based
first-order non-projective parsers, running at $O(n^2)$ time.\footnote{CLE has faster implementations:
$O(m+nlogn)$ \cite{Gabow:86} as well as $O(mlogn)$ for sparse graphs \cite{Tarjan:77},
both are super-linear in $n$ for connected graphs.
We refer here to the classical implementation employed by modern parsers
(e.g. \cite{McDonald:05b,Martins:13}).}

We experimented (\secref{experiments}) with 17 languages from the
CoNLL 2006 and 2007 shared tasks on multilingual dependency
parsing~\cite{buchholz2006conll,nilsson2007conll} and in three English setups.
Our results reveal that the two algorithms perform very similarly.
While the averaged unlabeled attachment accuracy score (UAS)
of the original parser is 0.97\% higher than ours,
in 11 of 20 test setups the number of sentences that are better parsed by our parser
is larger than the number of sentences that are better parsed by the original parser.

Importantly, in this work we present an edge-linear first-order dependency parser which achieves similar accuracy to the existing one, making it an excellent candidate to be used for efficient MST computation in $k$-best trees methods, or to be utilized as an
inference/initialization subroutine as a part of more complex approximation frameworks such as belief propagation. In addition, our model produces a different solution compared to the existing one (see Table~\ref{head-to-head}), paving the way for using methods such as dual decomposition to combine these two models into a superior one.

Undirected inference has been recently explored
in the context of transition based parsing \cite{gomez:12,gomez:15},
with the motivation of preventing the propagation of erroneous early edge directionality
decisions to subsequent parsing decisions. Yet,
to the best of our knowledge this is the first paper to address undirected inference
for graph based dependency parsing. Our motivation and
algorithmic challenges are substantially different from those of the earlier transition based work.

\isection{Undirected MST with the Boruvka Algorithm}{MST}

In this section we define the MST problem in undirected graphs.
We then discuss the Burovka algorithm~\cite{boruvka:26,nesetril:01}
which forms the basis for the randomized algorithm of~\cite{Karger:95} we employ in this paper.
In the next section we will describe the \newcite{Karger:95} algorithm in more details.

\paragraph{Problem Definition.}
For a connected undirected graph $G(V,E)$, where $V$ is the set
of $n$ vertices and $E$ the set of $m$ weighted edges, the MST problem is defined as finding the
sub-graph of $G$ which is the tree (a connected acyclic graph) with the
lowest sum of edge weights. The opposite problem -- finding the maximum
spanning tree -- can be solved by the same algorithms used for the minimum
variant by simply negating the graph's edge weights.

\paragraph{Graph Contraction.}

In order to understand the Boruvka algorithm, let us first define the {\it Graph Contraction} operation.
For a given undirected graph $G(V,E)$ and a subset $\tilde{E} \subseteq E$,
this operation creates a new graph, $G_C(V_C,E_C)$. In this new graph, $V_C$
consists of a vertex for each connected component in $\tilde{G}(V,\tilde{E})$
(these vertices are referred to as \textit{super-vertices}).
$E_C$, in turn, consists of one edge, $(\hat{u},\hat{v})$,
for each edge $(u,v) \in E \setminus \tilde{E}$, where $\hat{u},\hat{v} \in V_C$
correspond to $\tilde{G}$'s connected components
to which $u$ and $v$ respectively belong.
Note that this definition may result in multiple edges between two vertices in
$V_C$ (denoted \textit{repetitive edges})
as well as in edges from a vertex in $V_C$ to itself (denoted \textit{self edges}).

\begin{algorithm}
\hrule
\vspace{0.1cm}
Contract\_graph
\begin{algorithmic}
	\REQUIRE a graph $G(V,E)$, a subset $\tilde{E}\subseteq E$
	\STATE $C \leftarrow$ connected components of $\tilde{G}(V,\tilde{E})$
	\RETURN $G_C(C,E \setminus \tilde{E})$
\end{algorithmic}
\hrule
\vspace{0.1cm}
Boruvka-step
\begin{algorithmic}[1]
	\REQUIRE a graph $G(V,E)$
	\FORALL{$(u,v) \in E$}
		\IF{$w(u,v) < w(u.minEdge)$}
			\STATE $u.minEdge \leftarrow (u,v)$
		\ENDIF
		\IF{$w(u,v) < w(v.minEdge)$}
			\STATE $v.minEdge \leftarrow (u,v)$
		\ENDIF
	\ENDFOR
	\FORALL{$v \in V$}
		\STATE $E_m \leftarrow E_m \cup \{v.minEdge\}$
	\ENDFOR
	\STATE $G_B(V_B,E_B) \leftarrow$ {\small Contract\_graph}($G(V,E)$,$E_m$)
	\STATE Remove from $E_B$ self edges and non-minimal repetitive edges
	\RETURN $G_B(V_B,E_B),E_m$
\end{algorithmic}
\hrule
\caption{The basic step of the Boruvka algorithm for the undirected MST problem.}
\label{alg1}
\end{algorithm}

\begin{figure}
\centerline{
\begin{tabular}{cc}
\includegraphics[scale=0.65]{boruvka_a.png} & \includegraphics[scale=0.65]{boruvka_b.png} \\
(a) & (b)
\end{tabular}
}
\centerline{
\begin{tabular}{cc}
\includegraphics[scale=0.65]{boruvka_c.png} & \includegraphics[scale=0.65]{boruvka_d.png} \\
(c) & (d)
\end{tabular}
}
\caption[]{An illustration of a {\it Boruvka step}:
(a) The original graph; (b) Choosing the minimal edge for each vertex (marked in red);
(c) The contracted graph; (d) The contracted graph after removing one self edge
and two non-minimal repetitive edges.}
\label{Boruvka-example}
\end{figure}

\paragraph{The Boruvka-Step.}

Next, we define the basic step of the Borukva algorithm
(see example in Figure~\ref{Boruvka-example} and pseudocode in Algorithm \ref{alg1}).
In each such step, the algorithm creates a subset $E_m \subset E$ by selecting
the minimally weighted edge for each vertex in the input graph $G(V,E)$
(Figure~\ref{Boruvka-example} (a,b) and Algorithm~\ref{alg1} (lines 1-11)).
Then, it performs the contraction operation on the graph $G$ and $E_m$
to receive a new graph $G_B(V_B,E_B)$ (Figure~\ref{Boruvka-example} (c) and Algorithm~\ref{alg1} (12)).
Finally, it removes from $E_B$ all self-edges and repetitive
edges that are not the minimal edges between the vertices $V_B$'s which they connect
(Figure~\ref{Boruvka-example} (d) and Algorithm~\ref{alg1} (13)).
The set $E_m$ created in each such step is guaranteed to
consist only of edges that belong to $G$'s MST and is therefore also returned by the
Boruvka step.

The Boruvka algorithm runs successive Boruvka-steps until it is left with
a single super-vertex. The MST of the original graph $G$ is given by the
unification of the $E_m$ sets returned in each step. The resulting computational complexity
is $O(m\log{n})$~\cite{nesetril:01}.
We now turn to describe how the undirected MST problem can
be solved in a time practically linear in the number of graph edges.

\isection{Undirected MST in Edge Linear Time}{state-of-the-art}

There are two algorithms that solve the undirected MST problem
in time practically linear in the number of edges in the input graph.
These algorithms are based on substantially different approaches:
one is deterministic and the other is randomized
\footnote {Both these algorithms deal with a slightly more general case where the
graph is not necessarily connected, in which case the
\textit{minimum spanning forest (MSF)} is computed.
In our case, where the graph is connected, the MSF reduces
to an MST. }.

The complexity of the first, deterministic,
algorithm~\cite{Chazelle:00a,Pettie:02} is $O(m \cdot \alpha(m,n))$, where $\alpha(m,n)$
is a natural inverse of Ackermann's function, whose value for any
practical values of $n$ and $m$ is lower than 5.
As this algorithm employs very complex data-structures, we
do not implement it in this paper.

The second, randomized, algorithm~\cite{Karger:95} has an expected run time of
$O(m+n)$ (which for connected graphs is $O(m)$), and this run time is achieved with
a high probability of $1-exp(-\Omega(m))$.~\footnote{This complexity analysis
is beyond the scope of this paper.}
In this paper we employ only
this algorithm for first-order graph-based parsing inference, and hence
describe it in details in this section.

\paragraph {Definitions and Properties.}
We first quote two properties of undirected graphs~\cite{Tarjan:83}: (1) The {\bf cycle property}: The
heaviest edge in a
cycle in a graph does not appear in the MSF; and
(2) The {\bf cut property}: For any proper nonempty subset $V'$ of
the graph vertices, the lightest edge with exactly one endpoint in $V'$
is included in the MSF.

We continue with a number of definitions and observations.
Given an undirected graph $G(V,E)$ with weighted edges, and a forest $F$ in that
graph, $F(u,v)$ is the path in that forest between $u$ and $v$ (if such a path exists),
and $s_F(u,v)$ is the maximum weight of an edge in $F(u,v)$
(if the path does not exist then $s_F(u,v) = \infty$).
An edge $(u,v) \in E$ is called {\it F-heavy} if $s(u,v) > s_F(u,v)$,
otherwise it is called {\it F-light}.
An alternative equivalent definition is that an edge is {\it F-heavy}
if adding it to $F$ creates a cycle in which it is the heaviest edge.
An important observation (derived from the cycle property)
is that for any forest $F$, no {\it F-heavy} edge can possibly be a part of an MSF for $G$.
It has been shown that given a forest $F$,
all the {\it F-heavy} edges in $G$ can be found in $O(m)$ time~\cite{Dixon:92,King:95}.

\begin{algorithm}[t!]
\hrule
\vspace{0.1cm}
Randomized\_MSF
\begin{algorithmic}[1]
	\REQUIRE a graph $G(V,E)$
	\IF{$E$ is empty}
		\RETURN $\emptyset$
	\ENDIF
	\STATE $G_C(V_C,E_C),E_m \leftarrow$ Boruvka-step2($G$)
	\FORALL{$(u,v) \in E_C$}
		\IF{coin-flip == head}
			\STATE $E_s \leftarrow E_s \cup \{(u,v)\}$
			\STATE $V_s \leftarrow V_s \cup \{u,v\}$
		\ENDIF
	\ENDFOR
	\STATE $F \leftarrow$ Randomized\_MSF($G_s(V_s,E_s)$)
	\STATE remove all {\it F-heavy} edges from $G_C(V_C,E_C)$
	\STATE $F_C \leftarrow$ Randomized\_MSF($G_C(V_C,E_C)$)
	\RETURN $F_C \cup E_m$
\end{algorithmic}
\hrule
\caption {Pseudocode for the Randomized MSF algorithm of\protect\cite{Karger:95}.}
\label{alg2}
\end{algorithm}

\paragraph {Algorithm.}

The randomized algorithm can be outlined as follows
(see pseudocode in algorithm~\ref{alg2}):
first, two successive Boruvka-steps are applied to the graph
(line 4, \textit{Boruvka-step2} stands for two successive Boruvka-steps),
reducing the number of vertices by (at least)
a factor of 4 to receive a contracted graph $G_C$ and an edge set
$E_m$ (\secref{MST}).
Then, a subgraph $G_s$ is randomly constructed, such that each edge in
$G_C$, along with the vertices which it connects, is included in $G_s$
with probability $\frac{1}{2}$ (lines 5-10).
Next, the algorithm is recursively applied to $G_s$ to obtain
its minimum spanning forest $F$ (line 11).
Then, all {\it F-heavy} edges are removed from $G_C$ (line 12),
and the algorithm is recursively applied to the resulting graph to obtain
a spanning forest $F_C$ (line 13). The union of that forest
with the edges $E_m$ forms the requested spanning forest (line 14).

\paragraph{Correctness.}

The correctness of the algorithm is proved by induction.
By the cut property, every edge returned by the Boruvka step (line 4),
is part of the MSF. Therefore, the rest of the edges in the original
graph's MSF form an MSF for the contracted graph.
The removed {\it F-heavy} edges are, by the cycle property, not part of
the MSF (line 12). By the induction assumption, the MSF of the remaining graph
is then given by the second recursive call (line 13).

\isection{Undirected MST Inference for Dependency Parsing}{parser}

There are several challenges in the construction of an \textit{undirected MST parser}:
an MST parser that employs an undirected MST algorithm for inference.\footnote{Henceforth,
we refer to an MST parser that employs a directed MST algorithm for inference
as \textit{directed MST parser}.}
These challenges stem from the mismatch between the undirected nature of the inference algorithm and the
directed nature of the resulting parse tree.

The first problem is that of \textit{undirected encoding}.
Unlike directed MST parsers that explicitly encode the directed nature of dependency parsing into a
directed input graph to which an MST algorithm is applied \cite{McDonald:05b},
an undirected MST parser needs to encode directionality information into an undirected graph.
In this section we consider two solutions to this problem.

The second problem is that of \textit{scheme conversion}.
The output of an undirected MST algorithm
is an undirected tree while the dependency parsing problem requires finding
a directed parse tree. In this section
we show that for rooted undirected spanning trees there is only one way to
define the edge directions under the
constraint that the root vertex has no incoming edges and that each non-root
vertex has exactly one incoming edge in the resulting
directed spanning tree. As dependency parse trees obey
the first constraint and the second constraint is a definitive property
of directed trees,
the output of an undirected MST parser can be transformed
into a directed tree using a simple $O(n)$ time procedure.

Unfortunately, as we will see in \secref{experiments}, even with our best
undirected encoding method, an undirected MST
parser does not produce directed trees of the same quality as its directed counterpart.
At the last part of this section we therefore present a simple, $O(n)$ time,
\textit {local enhancement procedure}, that improves the score of the
directed tree generated from the output of the undirected MST parser with respect to the
edge scores of a standard directed MST parser.
That is, our procedure improves the output of the undirected MST parser
with respect to a directed model without having to compute the MST of the latter,
which would take $O(n^2)$ time.

We conclude this section with a final remark stating that the output class of our inference algorithm
is non-projective. That is, it can generate all possible parse trees, projective and non-projective.

\paragraph{Undirected Encoding}

Our challenge here is to design an encoding scheme that encodes
directionality information into the graph of the undirected MST problem.
One approach would be to compute directed edge weights according to a feature
representation scheme for directed edges
(e.g. one of the schemes employed by existing directed MST parsers) and then
transform these directed weights into undirected ones.

Specifically, given two vertices $u$ and $v$ with directed edges $(u,v)$ and $(v,u)$,
weighted with $s_d(u,v)$ and $s_d(v,u)$ respectively, the goal is to compute the weight
$s_u(\hat{u,v})$ of the undirected edge $(\hat{u,v})$ connecting them in the undirected graph. We
do this using a pre-determined function $f:\mathbb{R} \times \mathbb{R} \rightarrow \mathbb{R}$, such that
$f(s_d(u,v),s_d(v,u)) = s_u(\hat{u,v})$.
$f$ can take several forms including mean, product and so on.
In our experiments the mean proved to be the best choice.

Training with the above approach is implemented as follows. $w$, the parameter
vector of the parser, consists of the weights of \textit{directed features}.
At each training iteration, $w$ is used for the computation
of $s_d(u,v) = w \cdot \phi(u,v)$ and $s_d(v,u) = w \cdot \phi(v,u)$
(where $\phi(u,v)$ and $\phi(v,u)$ are the feature representations of these directed edges).
Then, $f$ is applied to compute the undirected edge score $s_u(\hat{u,v})$.
Next, the undirected MST algorithm is run on the resulting weighted undirected
graph, and its output MST is transformed into a directed tree (see below).
Finally, this directed tree is used for the update of $w$ with
respect to the gold standard (directed) tree.

At test time, the vector $w$ which resulted from the training process
is used for $s_d$ computations. Undirected graph construction, undirected MST computation
and the undirected to directed tree conversion process are conducted exactly as in training.~\footnote
{In evaluation setup experiments we also considered a variant of this model where the training
process utilized directed MST inference. As this variant performed
poorly, we exclude it from our discussion in the rest of the paper.}

Unfortunately, preliminary experiments in our development setup revealed
that this approach yields parse trees of much lower quality
compared to the trees generated by the directed MST parser that employed the original directed feature set.
In \secref{experiments} we discuss these results in details.

An alternative approach is to employ an undirected feature set. To implement this
approach, we employed the feature set of the MST parser
(\cite {McDonald:05a}, Table 1) with one difference: some of the features are directional,
distinguishing between the properties of the source (parent) and the target (child) vertices.
We stripped those features from that information, which resulted in an undirected version of the feature set.

Under this feature representation, training with undirected inference is simple.
$w$, the parameter vector of the parser, now consists of the weights of
\textit{undirected features}. Once the undirected MST is computed by an undirected
MST algorithm, $w$ can be updated with respect to an undirected variant of the gold parse trees.
At test time, the algorithm constructs an undirected graph using the vector $w$ resulted from
the training process. This graph's undirected MST is computed and then
transformed into a directed tree.

Interestingly, although this approach does not explicitly encode edge directionality information
into the undirected model, it performed very well in our experiments
(\secref{experiments}), especially when combined with
the local enhancement procedure described below.

\begin{figure}
\centerline{
\begin{tabular}{cccc}
\includegraphics[scale=0.8]{directing_a.png} & \includegraphics[scale=0.8]{directing_b.png} & \includegraphics[scale=0.8]{directing_c.png} & \includegraphics[scale=0.8]{directing_d.png} \\
(a) & (b) & (c) & (d)
\end{tabular}
}
\caption[]{An illustration of directing an undirected tree, given a constrained root vertex:
(a) The initial undirected tree;
(b) Directing the root's outgoing edge;
(c) Directing the root's child's outgoing edges;
(d) Directing the last remaining edge, resulting in a directed tree.}
\label{directing-fig}
\end{figure}

\begin{figure}
\centerline{
\begin{tabular}{ccc}
\includegraphics[scale=0.6]{trick_a.png} & \includegraphics[scale=0.6]{trick_b.png} & \includegraphics[scale=0.6]{trick_c.png} \\
(a) & (b) & (c)
\end{tabular}
}
\centerline{
\begin{tabular}{cc}
\includegraphics[scale=0.6]{trick_d.png} & \includegraphics[scale=0.6]{trick_e.png}\\
(d) & (e)
\end{tabular}
}
\caption{An illustration of the local enhancement procedure
for an edge $(u,v)$ in the du-tree.
Solid lines indicate edges in the du-tree,
while dashed lines indicate edges not in the du-tree.
(a) Example subtree;
(b) Evaluate $gain = s_d(t,u) + s_d(u,v) - (s_d(t,v) + s_d(v,u)) = 4+3-(5+1) = 1$;
(c) In case a modification is made, first replace
$(u,v)$ with $(v,u)$; and then
(d) Remove the edge $(t,u)$; and, finally,
(e) Add the edge $(t,v)$.}
\label{trick-fig}
\end{figure}

\paragraph{Scheme Conversion}

Once the undirected MST is found, we need to direct its edges in order for the end result to be a
directed dependency parse tree.
Following a standard practice in graph-based dependency parsing (e.g.~\cite{McDonald:05b}),
before inference is performed we add a dummy root vertex to the initial input graph
with edges connecting it to all of the other vertices in the graph.
Consequently, the final undirected tree will have a designated root vertex.
In the resulting directed tree, this vertex is constrained to have only outgoing edges. As observed by \newcite{gomez:12},
this effectively forces the direction for the rest of the edges in the tree.

Given a root vertex that follows the above constraint, and together with the definitive property of directed trees
stating that each non-root vertex in the graph has exactly one incoming edge,
we can direct the edges of the undirected tree using a simple BFS-like algorithm (Figure~\ref{directing-fig}).
Starting with the root vertex, we mark its undirected edges as outgoing,
mark the vertex itself as \textit{done} and its descendants as \textit{open}.
We then recursively repeat the same procedure for each open vertex until there
are no such vertices left in the tree, at which point we have a directed tree.
Note that given the constraints on the root vertex, there is no other way to
direct the undirected tree edges. This procedure
runs in $O(n)$ time, as it requires a constant number of operations for each
of the $n-1$ edges of the undirected spanning tree.

In the rest of the paper we refer to the \emph{directed} tree generated by
the undirected and directed MST parsers as \textit{du-tree} and
\textit{dd-tree} respectively.

\paragraph{Local Enhancement Procedure}

As noted above, experiments in our development setup
(\secref{experiments}) revealed that the directed parser
performs somewhat better than the undirected one.
This motivated us to develop a local enhancement procedure that
improves the tree produced by the undirected model with respect
to the directed model without compromising our $O(m)$ run time.
Our enhancement procedure is motivated by development
experiments, revealing the much smaller gap between the quality of the du-tree and dd-tree
of the same sentence under undirected evaluation compared to directed evaluation
(\secref{experiments} demonstrates this for test results).

For a du-tree that contains the vertex $u$ and the edges $(t,u)$
and $(u,v)$, we therefore consider the replacement of $(u,v)$ with $(v,u)$.
Note that after this change our graph would no longer be a directed tree,
since it would cause $u$ to have two parents, $v$ and $t$, and $v$ to have
no parent. This, however, can be rectified by replacing the edge $(t,u)$
with the edge $(t,v)$.

It is easy to infer whether this change results in a better (lower weight) spanning
tree under the directed model by computing the equation:
$gain = s_d(t,u) + s_d(u,v) - (s_d(t,v) + s_d(v,u))$, where $s_d(x,y)$
is the score of the edge $(x,y)$ according to the directed model.
This is illustrated in Figure~\ref{trick-fig}.

Given the du-tree, we traverse its edges and compute the above gain for each.
We then choose the edge with the maximal positive gain,
as this forms the maximal possible decrease in the directed model score using modifications
of the type we consider, and perform the corresponding modification. In our experiments we performed this
procedure five times per inference problem.\footnote{This hyperparameter was estimated once on our
English development setup, and used for all 20 multilingual test setups.}
This procedure performs a constant number of operations
for each of the $n-1$ edges of the du-tree, resulting in $O(n)$ run time.

\paragraph {Output Class.}

Our undirected MST parser is non-projective.
This stems from the fact that the undirected MST algorithms we discuss in \secref{state-of-the-art}
do not enforce any structural constraint, and particularly the non-crossing constraint,
on the resulting undirected MST.
As the scheme conversion (edge directing) and the local enhancement procedures described in this section do
not enforce any such constraint as well, the resulting tree can take any possible structure.

\isection{Experiments and Results}{experiments}

\begin{table*}
\scriptsize
\centering
\begin{tabular}{ c|c|c|c|c|c|c|c|c|c|c }
   & Swedish & Danish & Bulgarian & Slovene & Chinese & Hungarian & Turkish & German & Czech & Dutch \\
   \hline
		D-MST	        & 87.7/88.9	& 88.5/89.5	& 90.4/90.9	& 80.4/83.4	& 86.1/87.7	& 82.9/84.3	& 75.2/75.3	& 89.6/90.2	& 81.7/84.0	& 81.3/83.0 \\
		\hline
		U-MST-uf-lep	& 86.9/88.4	& 87.7/88.9	& 89.7/90.6	& 79.4/82.8	& 84.8/86.7	& 81.8/83.3	& 74.9/75.3	& 88.7/89.5	& 79.6/82.5	& 78.7/80.7 \\
		\hline
		U-MST-uf	& 84.3/87.8	& 85.1/89.0	& 87.0/90.2	& 76.1/82.4	& 81.1/86.4	& 79.9/82.9	& 73.1/75.0	& 86.9/89.0	& 76.1/81.9	& 73.4/80.5 \\
		\hline
		U-MST-df	& 72.0/79.2	& 74.3/82.9	& 69.5/81.4	& 66.8/75.8	& 65.9/76.5	& 68.2/72.1	& 57.4/62.6	& 77.7/82.5	& 57.3/70.9	& 59.0/71.3 \\
   \hline
	 \hline
	                          & Japanese & Spanish & Catalan & Greek & Basque & Portuguese & Italian & PTB & QBank & GENIA \\
   \hline
                D-MST	        & 92.5/92.6	& 83.8/86.0	& 91.8/92.2	& 82.7/84.9	& 72.1/75.8	& 89.2/89.9   & 83.4/85.4	& 92.1/92.8	& 95.8/96.3	& 88.9/90.0\\
		\hline
		U-MST-uf-lep	& 92.1/92.2	& 83.5/85.9	& 91.3/91.9	& 81.8/84.4	& 71.6/75.8	& 88.3/89.3   & 82.4/84.7	& 90.6/91.7	& 95.6/96.2	& 87.2/88.9 \\
		\hline
		U-MST-uf	& 91.4/92.4	& 80.4/85.4	& 89.7/91.7	& 78.7/84	& 68.8/75.4	& 85.8/89.3   & 79.4/84.4	& 88.5/91.8	& 94.8/96.0	& 85.0/89.0 \\
		\hline
		U-MST-df	& 74.4/85.2	& 73.1/81.3	& 73.1/83.5	& 71.3/78.7	& 62.8/71.4	& 67.9/79.7   & 65.2/77.2	& 77.2/85.4	& 89.1/92.9	& 72.4/81.6 \\
         \hline
         \hline
\end{tabular}
\caption{\label{results_directed} Directed/undirected UAS for the various parsing models of this paper.}
\end{table*}

\paragraph{Experimental setup}

\begin{table*}[t!]
\scriptsize
\centering
\begin{tabular}{ c|c|c|c|c|c|c|c|c|c|c }
        & Swedish & Danish & Bulgarian & Slovene & Chinese & Hungarian & Turkish & German & Czech & Dutch \\
\hline
D-MST & 20.6 & 20.8 & 15.1 & 25.4 & 15.5 & 26.4 & 22.3 & 21.3 & 29.7 & 27.7 \\
\hline
U-MST-uf-lep & 18.0 & 24.5 & 22.1 & 29.6 & 16.7 & 27.2 & 19.3 & 17.9 & 26.2 & 24.4 \\
\hline
Oracle & 88.9   & 89.7   & 91.6   & 81.9   & 87.8   & 83.9   & 77.1   & 90.6 & 82.8   & 82.8 \\
       & (+1.2) & (+1.4) & (+1.2) & (+1.5) & (+1.7) & (+1) & (+1.9) & (+1) & (+1.1) & (+1.5) \\
\hline
\hline
        & Japanese & Spanish & Catalan & Greek & Basque & Portuguese & Italian & PTB & QBank & GENIA \\
\hline
D-MST & 5.7 & 26.7 & 23.4 & 28.9 & 23.4 & 22.6 & 22.5 & 27.8 & 5.3 & 33.7 \\
\hline
U-MST-uf-lep & 4.0 & 30.1 & 26.3 & 30.5 & 30.8 & 21.9 & 24.9 & 20.9 & 6.0 & 23.8 \\
\hline
Oracle & 93.1   & 84.8 & 92.6   & 83.9   & 74.1  & 89.9   & 84.4   & 92.8   & 96.4   & 89.7   \\
       & (+0.6) & (+1) & (+0.8) & (+1.2) &(+2) & (+0.7) & (+1) & (+0.7) & (+0.8) & (+0.8)  \\
\hline
\end{tabular}
\caption{\label{head-to-head} Top two lines (per language): percentage of sentences for which each of the
models performs better than the other according to the directed UAS.
Bottom line (Oracle): Directed UAS of an oracle model that selects the parse tree of
the best performing model for each sentence. Improvement over the directed UAS score of D-MST
is given in parenthesis.}
\end{table*}

We evaluate four models:
(a) The original directed parser (D-MST, \cite{McDonald:05b});
(b) Our undirected MST parser with undirected features and with
the local enhancement procedure (U-MST-uf-lep);\footnote{The directed edge weights
for the local enhancement procedure ($s_d$ in \secref{parser})
were computed using the trained D-MST parser.}
(c) Our undirected MST parser with undirected features but without
the local enhancement procedure (U-MST-uf); and
(d) Our undirected MST parser with directed features (U-MST-df).
All models are implemented within the MSTParser
code\footnote{\url{http://www.seas.upenn.edu/~strctlrn/MSTParser/MSTParser.html}}.

The MSTParser does not prune its input graphs.
To demonstrate the value of undirected parsing for sparse input graphs,
we implemented the length-dictionary pruning strategy
which eliminates all edges longer than the maximum length observed for each \textit{directed}
head-modifier POS pair in the training data. An undirected
edge $\hat{(u,v)}$ is pruned $iff$ both directed edges $(u,v)$ and $(v,u)$
are to be pruned according to the pruning method.
To estimate the accuracy/graph-size tradeoff provided by undirected parsing (models (b)-(d)),
we apply the pruning strategy only to these models leaving the the D-MST model (model (a)) untouched.
This way D-MST runs on a complete directed graph with $n^2$ edges.

Our models were developed in a monolingual setup: training on sections 2-21 of  WSJ
PTB~\cite{marcus:93} and testing on section 22.
The development phase was devoted to the various decisions detailed throughout this paper
and to the tuning of the single hyperparameter: the number of times the local enhancement procedure
is executed.

We tested the models in 3 English and 17 multilingual setups. The English setups are:
(a) \textit{PTB}: training on sections 2-21 of the
WSJ PTB and testing on its section 23;
(b) \textit{GENIA}: training with a random sample of 90\% of the 4661
GENIA corpus~\cite{ohta2002genia} sentences
and testing on the other 10\%;
and (c) \textit{QBank}: a setup identical to (b) for the 3987
QuestionBank~\cite{judge2006questionbank} sentences.
Multilingual parsing was performed with the multilingual datasets of the
CoNLL 2006~\cite{buchholz2006conll} and 2007~\cite{nilsson2007conll}
shared tasks on multilingual dependency parsing, following their standard
train/test split. Following previous work, punctuation was excluded from the
evaluation.

Length-dictionary pruning reduces the number of undirected edges by 27.02\% on average across
our 20 setups (std = 11.02\%, median = 23.85\%), leaving an average of 73.98\% of the edges in the undirected graph.
In 17 of 20 setups the reduction is above 20\%. Note that the number of edges in
a complete directed graph is twice the number in its undirected counterpart.
Therefore, on average, the number of input edges in the pruned undirected models
amounts to $\frac{73.98\%} {2} = 36.49\%$ of the number of edges in the complete directed graphs.
In fact, every edge-related operation (such as feature extraction) in the undirected model is actually
performed on half of the number of edges compared to the directed model, saving run-time not only
in the MST-inference stage but in every stage involving these operations. In addition, some pruning
methods, such as length-dictionary pruning (used in this work) perform feature extraction only for existing
(un-pruned) edges, meaning that any reduction in the number of edges also reduces feature extraction operations.

For each model we report the standard directed unlabeled attachment accuracy score (D-UAS).
In addition, since this paper explores the value of undirected inference for
a problem that is directed in nature, we also report the
undirected unlabeled attachment accuracy score (U-UAS), hoping that these
results will shed light on the differences between the trees generated by the different models.

\paragraph{Results}

Table~\ref{results_directed} presents our main results.
While the directed MST parser (D-MST) is the best performing model
across almost all test sets and evaluation measures, it outperforms our best model,
U-MST-uf-lep, by a very small margin.

Particularly, for D-UAS, D-MST outperforms U-MST-uf-lep by up to
1\% in 14 out of 20 setups (in 6 setups the difference is up to 0.5\%).
In 5 other setups the difference between the models is between 1\% and 2\%, and only
in one setup it is above 2\% (2.6\%). Similarly, for
U-UAS, in 2 setups the models achieve the same performance, in
15 setups the difference is less than 1\% and in the other setups the differences is 1.1\% - 1.5\%.
The average differences are 0.97\% and 0.67\% for D-UAS and U-UAS respectively.

The table further demonstrates the value of the local enhancement procedure.
Indeed, U-MST-uf-lep outperforms U-MST in all 20 setups in D-UAS evaluation
and in 15 out of 20 setups in U-UAS evaluation
(in one setup there is a tie). However, the improvement this procedure provides
is much more noticeable for D-UAS, with an averaged improvement
of 2.35\% across setups, compared to an averaged U-UAS improvement
of only 0.26\% across setups.
While half of the changes performed by the local enhancement procedure are in edge directions,
its marginal U-UAS improvement indicates that almost all of its power
comes from edge direction changes. This calls for an improved enhancement procedure.

Finally, moving to directed features (the U-MST-df model), both D-UAS
and U-UAS substantially degrade, with more noticeable
degradation in the former. We hypothesize that this stems from the idiosyncrasy
between the directed parameter update and the undirected inference in this model.

Table~\ref{head-to-head} reveals the complementary nature of our
U-MST-uf-lep model and the classical D-MST:
each of the models outperforms the other on an average of 22.2\% of the sentences across test setups.
An oracle model that selects the parse tree of the best model for each sentence would improve
D-UAS by an average of 1.2\% over D-MST across the test setups.

The results demonstrate the power of first-order graph-based dependency parsing with
undirected inference. Although using a substantially different inference algorithm,
our U-MST-uf-lep model performs very similarly to the standard MST parser which
employs directed MST inference.

\isection{Discussion}{discussion}

We present a first-order graph-based dependency parsing model which runs in edge linear time
at expectation and with very high probability.
In extensive multilingual experiments our model performs very similarly to a standard directed
first-order parser. Moreover, our results demonstrate the complementary nature of the models, with our model
outperforming its directed counterpart on an average of 22.2\% of the test sentences.

Beyond its practical implications, our work provides a novel intellectual contribution in demonstrating
the power of undirected graph based methods in solving an NLP problem that is directed in nature.
We believe this contribution has the potential to affect future research on additional NLP problems.

The potential embodied in this work extends to a number of promising research directions:
\begin{itemize}
\item Our algorithm may be used for efficient MST computation in $k$-best trees methods which are instrumental in
margin-based training algorithms. For example, \newcite{McDonald:05b} observed that $k$ calls to the CLU algorithm might
prove to be too inefficient; our more efficient algorithm may provide the remedy.
\item It may also be utilized as an inference/initialization subroutine as a part of more complex approximation frameworks
such as belief propagation (e.g. \newcite{Smith:08}, \newcite{Gormley:15}).
\item Finally, the complementary nature of the directed and undirected parsers motivates the development of methods for
their combination, such as dual decomposition (e.g. \newcite{Rush:10}, \newcite{Koo:10a}). Particularly, we have shown that our undirected inference algorithm converges to a different solution than the standard directed solution while still maintaining high quality (Table~\ref{head-to-head}). Such techniques can exploit this diversity to produce a higher quality unified solution.
\end{itemize}

We intend to investigate all of these directions in future work. In addition, we are currently exploring potential extensions of the techniques presented in this paper to higher order, projective and non-projective, dependency parsing.

\section*{Acknowledgments}
The second author was partly supported by a GIF Young Scientists' Program   grant No. I-2388-407.6/2015 - Syntactic Parsing in Context.

\bibliographystyle{acl2016}

redpajama_set_name: RedPajamaArXiv


text:
Bar chief told to retract statement in 14 days - or 100 lawyers will demand his resignation
Sharanpal Singh Randhawa
KUALA LUMPUR: Malaysian Bar president Christopher Leong must retract his statement on the Federal Court's decision to uphold Datuk Seri Anwar Ibrahim's sodomy conviction within 14 days.
If not, 100 members of the Bar from various states in the peninsula said they would seek his resignation as well as call upon the Attorney-General to initiate contempt proceedings against him for questioning the judgment.
Speaking on behalf of the 100, senior lawyer Tan Sri Abdul Aziz Abdul Rahman told a press conference yesterday that they deemed Leong's statement to be a direct attack on the Federal Court.
"As practicing lawyers, we are very concerned when the Bar president makes such disparaging remarks about our judicial system and the courts, in the name of representing the Malaysian Bar," he claimed.
Anwar Ibrahim faces final judgment at sodomy trial
Fellow lawyer, Datuk Mohd Noor Amin, said Leong's statement on Feb 11 implied that Anwar was "persecuted, not prosecuted" and also questioned why the complainant, Mohd Saiful Bukhari Azlan, was not charged with abettment under Sections 377A and 377B of the Penal Code read together with Section 109.
"This is an utterly outrageous statement for even a lay person to make, what more coming from the president of the Malaysian Bar.
"What is obvious from the said press release, is that, in one breath Leong admits having not read the judgment, and yet in the same breath, the day after the decision, he makes disparaging remarks that the judgment of the Federal Court was without any basis in law," he claimed.
They urged Leong to apologise to the judiciary and judges publicly, communicate it to all international bodies and entities, and make it known that the views in the Feb 11 press release were his and not of the majority of the Bar.
When contacted, Leong replied via text message that members were welcome to air their views.
"We also encourage these practising members to attend the Malaysian Bar AGM (Annual General Meeting) on Mac 14, and raise the matter there where many of our members will be present," he said.


text:
Home Travel Guide Main Attractions Site of the National Institute of the Peasant Movement
Site of the National Institute of the Peasant Movement
To train cadres to meet the needs of the rapidly developing worker-peasant movement in the First Revolutionary Civil War (1924-1927), Comrade Mao Tsetung headed and directed the National Institute of the Peasant Movement in Guangzhou from May 1926. The Institute enrolled 327 trainees from 20 provinces and regions in the country. Comrades Chou En-Iai, Hsiao Chu-nu, Peng Pai and Yun Tai-Ying were among the teachers.
Studies at the Institute centred on Marxism-Leninism and the peasant question the basic questions of the Chinese revolution. Comrade Mao Tsetung personally lectured on the question of Chinese peasants, rural education and other subjects, and gave 'special lectures on his brilliant article 's analysis of the Classes in Chinese Society".
The Institute advocated a revolutionary style of study that integrated theory with practice. The trainees were organized in research groups according to regions to study the situation of the peasant movement in different provinces They were sent for experimental work in rural areas of Haifeng and Shaoguan in Guangdong Province where they took an active part in important political activities in society in order to temper themselves through actual struggle Comrade Mao Tsetung educated the trainees in the concept of "armed struggle" and gave them strict military training so they would become revolutionary cadres capable of doing propaganda work among the masses, organizing them and leading the armed struggle.
In September 1926 the courses concluded. Acting on Comrade Mao Tsetung's teachings the trainees left for various parts of the country to guide the peasant movement under the leadership of the Party and start the armed struggle against imperialism and feudalism.
After liberation, the Party and people's government repaired and restored the site of the Institute.
Opening Hours: 9 am ~ 4:30 pm (closed on Mondays)
Admission Fee: Free of charge
Address:42 Zhongshan Road, Yuexiu District, Guangzhou (广州市越秀区中山四路42号)


text:
NATURAL PATHWAYS TO LIVING WELL
Charming, historic Medford, NJ, was founded in 1847 and was selected by National Geographic as one of the nation's "small town treasures." Lumberton, founded in 1683 and incorporated in 1860, was originally named for its timber industry and sawmills. Medford Leas is an accredited Continuing Care Retirement Community (CCRC), spread across over 250 acres and two campuses, providing a uniquely fulfilling living experience to adults age 55 and older. Medford Leas also gives back to the community in many ways, click here to learn more.
QUAKER ROOTS, VALUES & CONNECTIONS
Medford Leas is sponsored by The Estaugh, a Quaker-related not-for-profit corporation founded in 1914 to serve the needs of older adults. Their purpose was to create a special community in which the principle of loving concern would guide their common effort. Medford Leas was born of that early vision and has grown in scope and in stature on The Estaugh's stable foundation. The Estaugh is guided by the teachings and beliefs of the Religious Society of Friends (Quakers), particularly the basic tenet that there is 'That of God' in everyone and the respect for the sanctity of every individual inherent in that belief. The Estaugh is further guided by the following values: community, stewardship, life balance, human resources and excellence
BOARD OF TRUSTEES & LEADERSHIP
The vision of Medford Leas is achieved by a highly respected and engaged board of trustees. The all-volunteer board is comprised of a maximum of 15 members and requires that a majority shall be Quakers and that at least one member shall be a resident of Medford Leas. No individual has any equity or beneficial interest in the community.The leadership team is comprised of senior living professionals providing residents and staff a welcoming atmosphere of mutual trust and respect.
Haley Driscoll
Haley Driscoll – Clerk
Haley Driscoll, a Moorestown Friends School alumna, studied interior design at Syracuse University, attended the architecture department summer program at Cornell University, and earned her BS in interior design from Drexel University in Philadelphia, Pennsylvania. She is a lifelong resident of Moorestown and member of Moorestown Monthly Meeting, where she serves on the property committee. Haley served as a member of the board of Cadbury at Cherry Hill for six years. She also serves on the board of directors of Colestown Cemetery. Currently she is an associate and the healthcare interior design practice leader at NORR, an international architectural and engineering firm in Philadelphia. She has been designing healthcare interiors for over 20 years and in 2008, led the design team that earned the International Interior Design Association award for best healthcare spaces. In 2010, she was a member of the team that won the ASHE Vista Award for new construction. Haley serves on the Estaugh Board of Trustees as Clerk, Chair of the Long-Range Planning Committee, and on the Executive Committee.
Davis Henderson
ASSOCIATE CLERK
Davis Henderson – Associate Clerk
Davis Henderson grew up in a Quaker family in Lansdowne, Pennsylvania and Silver Spring Maryland. He graduated from Westtown School and Hamilton College. Following his graduation from Hamilton, he joined the Peace Corps, where he served as volunteer in Ecuador, a staff member in Washington DC and later country director in Malaysia. Returning from overseas, Davis became human resources director at the American Red Cross national headquarters in Washington, DC. Davis continued his career with the Red Cross as European operations manager in Stuttgart, general manager of the Chicago chapter and finally as chief executive officer of the Tampa and Houston chapters. Davis became a resident of the Lumberton campus of Medford Leas in 2011 and is a regular attender of the Mt. Laurel Friends Meeting. He currently serves on the Estaugh Board of Trustees as Associate Clerk, and is on the Arboretum, Executive, and Chair of the Development Committee.
Marianne Johnston Esq.
RECORDING SECRETARY
Marianne Johnston, Esquire – Recording Secretary
Marianne Johnston is an elder law attorney; personal experience inspired her to practice in this area of the law after enjoying a long career as a corporate litigator with a large Philadelphia-based law firm. She has been recognized as a versatile, analytical problem solver with excellent writing and interpersonal skills. A graduate of Lafayette College for her undergraduate studies, Marianne worked in banking for a number of years before deciding to go to law school. She graduated from George Washington University Law School with honors before joining the litigation team of a Philadelphia law firm. Marianne is currently with the firm Price and Price, LLC in Haddonfield, NJ. Marianne is a member of the Elder Law Section of the New Jersey State Bar Association and an active member of a local church where she teaches Sunday school. Marianne currently serves on the Estaugh Board of Trustees as Recording Secretary and Chair of the Quality and Safety Committee.
Fred Melroy
Francis (Fred) Melroy – Treasurer
Francis Melroy is a member of Medford Monthly Meeting, Medford resident and active volunteer. Fred spent the majority of his career in Hospital Financial Administration, most recently NYU Medical Center from which he retired in April 2015. Fred is an active volunteer for Quaker organizations and brings a deep understanding of Quaker business practices. He serves as Treasurer for Medford Monthly Meeting, as Treasurer for the Friends Journal board, on the Finance Committee for Philadelphia Yearly Meeting and is also on the YMCA Camp Ockanickon board. Fred also served on the board and as Chair of the Board at Cadbury Senior Services Cherry Hill, until their sale. Fred holds a degree in finance from Rider University. He was recruited out of college by Dow Jones and is a Vietnam veteran. Fred is also an avid golfer and strong supporter of the Medford Leas Golf Outing. Fred Currently serves as Treasurer of the Estaugh Board of Trustees, Chair of the Finance Committee, and on the Executive and Development Committee.
Walter Evans
Walter Evans served as vice president in business development for 29 years with First Fidelity Bank (now Wachovia). Prior to retirement, he was a real estate sales representative for Edgar Realty in Moorestown, NJ where he received recognition for his sales accomplishments. He served as an active volunteer firefighter in Moorestown for 37 years and during his tenure he served as the chairman of the board of fire commissioners for 21 years. As a member of Relief Engine Company, he also served as the treasurer for over 20 years. For over 30 years, Walt has served on the Moorestown Township Board of Assessors – and as chairman for 20 years. In 1996, he was named citizen of the year for Moorestown. He is a member of the Moorestown Monthly Meeting and serves on various committees. Walt has been a member of the Estaugh Board of Trustees since 1989, and currently serves on the Finance Committee.
Diane Symcak
Diane Symcak is a Registered Professional Nurse with a Bachelor of Arts degree from Notre Dame of Maryland University (formerly known as the College of Notre Dame of Maryland). She also holds an Associates Degree in Applied Science from Mercer County Community College and she is a graduate of the Helene Fuld School of Nursing. Diane is licensed in the State of New Jersey.
Her nursing career started in the Emergency Rooms of two different hospitals in Trenton, New Jersey. After leaving hospital work, from 1994 until my retirement in 2019, she was employed at NJM Insurance Group in West Trenton, NJ in the Workers' Compensation and Underwriting
Divisions. While at NJM, she became the Supervisor of Medical Case Management which involved the coordination and management of care of serious and catastrophically injured patients.
During the final phase of her career at NJM (over 15 years), Diane was in the field of Healthcare Safety & Risk Management. This required her to travel throughout New Jersey, as well as parts of Pennsylvania and New York where she worked with large hospital systems, nursing homes, assisted living facilities, CCRC's & home care agencies who were our policyholders.
Working in the field of Healthcare Safety required her to have an understanding of the complete continuum of care, as well as a working knowledge of the various regulations (State and Federal, including OSHA) which govern the operation of the various organizations. Diane's job involved conducting surveys of the various facilities to identify areas of potential risk, as well as provide suggestions for prevention of injuries. She also was an active member of various policyholders' safety and quality committees, and often conducted presentations to Executive Leadership on safety and loss prevention topics. There were also several occasions where she was asked to speak at conferences on this topic, as well.
While working at NJM, Diane also achieved and maintained professional certifications in Rehabilitation Nursing, Case Management, Disability Management, and Occupational Health and Safety.
Since moving to the Lumberton Campus and following her retirement, Diane's time is occupied with working in the Thrift Store, being a volunteer transporter of residents (pre-pandemic), a Lumberton Cluster Leader and new resident "Greeter", a member of the Lumberton Council, as well as being a member of the Estaugh Board Safety and Quality subcommittee.
Although she has only lived at Lumberton since 2017, Diane has had an association with Medford Leas ever since they became a policyholder of NJM, which was approximately 20 years ago, and she says; "I can think of no greater endorsement of this community!"
Marlene Hall
Marlene Hall possesses more than 25 years of comprehensive human resources experience in the public and private sectors, specifically manufacturing and social service industries. She is currently the Corporate Human Resources Manager for The Stonhard Group, responsible for shaping the culture, recruiting, training and retaining a high performance engaged workforce, through effective leadership, development and HR practices and strategies. She grew up in Moorestown, attending the public schools. Marlene holds a Master of Science in HR Management, Minneapolis, MN. She is the current president of Moorestown's Farmdale Civic Association, a nonprofit neighboring civic league. Marlene currently serves on the Estaugh Board of Trustees Leadership Development Committee.
Naoji Moriuchi
Naoji Moriuchi brings to the Medford Leas board strong ties to South Jersey and Burlington county. He is a member of Moorestown Monthly Meeting and is co-owner and Broker Associate with Keller-Williams – Moorestown. Naoji grew up in Moorestown, graduated from Moorestown Friends School, and played on Moorestown's soccer and lacrosse club teams. Prior to joining Keller Williams Realty, Naoji spent 10 years at BT Edgar & Son Realtors. Over the course of his career he has obtained his Accredited Buyer's Representative designation, Graduate Relator Institute and is a licensed NJ real estate Broker. He is also a member of the Burlington Camden County Association of Realtors, NJ Association of Realtors, and the National Association of Realtors. Some of his accomplishments include: Features in MONEY Magazine, CNN.com and HGTV as a local real estate expert. In addition to receiving numerous sales awards and accolades for his real estate accomplishments, he served a four-year term, appointed by the Supreme Court of NJ, on the District IIIB Ethics Committee. Naoji is active in the community. He is currently a member of the Moorestown Breakfast Rotary and previously served as president and secretary.
Richard (Rick) W. Bell, Jr.
Rick is President and Founder of Planning Capital Management Corporation, a wealth management, investment advisory services firm that for over 25 years has developed and managed strategies for its clients. He has over 30 years experience working in both national brokerage firms and independent firms providing investment strategies and asset management solutions. His background also includes working in insurance and estate planning.
Rick majored in Finance and Economics and has completed the Certified Financial Planning program at the College of Financial Planning. He is a former member of the Board of Trustees of the Cherry Hill Regional Chamber of Commerce, Rowan College of Burlington County, and Previous Chairman of the Ambassadors Club, Cherry Hill Education Foundation, and the Cherry Hill Planning Board. Rick has also been a member of the Citizen's Advisory Board, Economic Development Advisory Committee and a Trustee of the Camden County Work Force Investment Board. Rick has also been active with charitable organizations such as the South Jersey Juvenile Diabetes Research Foundation and currently serves on the Estaugh Board of Trustees Finance Committee.
Scott R. Jones
Scott Jones is a retired president of Forest Capital Partners, a forest investment firm he cofounded in 2000. Prior to joining Forest Capital Partners, he was president and CEO at Timberland Growth Corporation; director of business development and acquisitions at John Hancock Mutual Life Insurance Company; and currently is a director with the publicly traded timberland REIT, Rayonier.
Upon completion of his graduate studies in forestry from Duke University, Jones first pursued a career as a field forester in Florida with a pulp and paper company. He later attended Northwestern University and earned a master's degree in business administration. Scott is one of our country's foremost experts in the field of forest investment and has acquired millions of acres over his career on behalf of institutional investors. His company, Forest Capital Partners, was fifth largest private landowner in the country.
Scott holds an undergraduate from the Elizabethtown College, a master's degree in forestry from Duke University and a master's degree in management from the Kellogg School at Northwestern University. He also serves on the Board of Visitors of the Nicholas School of the Environment at Duke University.
Scott and his wife, Barbara, have four children and reside near Boston, Massachusetts. He is a lifelong member of Medford Monthly Meeting as well as attending Wellesley Meeting near his Boston home. His mother, Irene Jones, and uncle, Earl Evens, are residents of Medford Leas.
Jeremy Vickers
Abby M. Loftus
Dr. James D'Amico
Lisa Heinkel
DIRECTOR OF HEALTH SERVICES
Kim Hoftiezer
Phil Pagliaro
Jane Weston
DIRECTOR OF DEVELOPMENT & COMMUNITY RELATIONS
Jennifer Zdunczyk
DIRECTOR OF DINING SERVICES
Guided by Quaker principles and a leader in quality care, Medford Leas' membership in professional organizations, such as: LeadingAge, Continuing Care Accreditation Commission, Accreditation Commission for Health Care, Peace Church Risk Retention Program, FSA Compliance and Risk Management and Friends Services Alliance is testimony to the commitment to support residents in living an engaged life.
Continuing Care Accreditation Commission (CARF)
Five Star Quality Ranking
SAGE CARE LGBT CULTURAL COMPETENCY TRAINING - PLATINUM LEVEL
Accreditation Commission for Health Care (ACHC)
Friends Services Alliance
FSA Compliance & Risk Management


Choosing the right Chiropractor is an important choice to make. Dr. Wassermann has been at his Fort Lauderdale location for 16 years!
Thank you for visiting the website of Fort Lauderdale Chiropractor Dr. Jonah A. Wassermann, a professional Fort Lauderdale chiropractor. In practice as well as online, Fort Lauderdale Florida 33314 chiropractor Dr. Wassermann strives for excellence through superior patient treatment, education and satisfaction.
Wassermann's office you will be treated with a warmth and caring that is so rare in today's healthcare system. Our office staff is extremely friendly and will make you feel like family. Dr. Wassermann is a Chiropractic Physician who is trained in the diagnosis and treatment of human ailments without the use of drugs or surgery.
Dr. Wassermann specializes in musculoskeletal conditions such as muscle strains, tendonitis, arthritis, disc herniations, sports injuries, work injuries and auto accidents. Dr. Wassermann can also help with headaches including migraines and chronic conditions such as fibromyalgia and lupus. We also offer nutritional counseling for weight loss and digestive ailments.
Dr. Wassermann's safe effective treatment methods include diversified chiropractic adjustment technique, massage therapy performed by a licensed massage therapist, stretching, muscle strengthening, exercise, traction, electric stimulation, ultrasound, hot packs, cold packs and nutritional counseling. We also have on site x-ray facilities. Dr. Wassermann works closely with medical doctors in a vast array of specialties so if he cannot treat your condition he will refer you to a physician who can.
Within our website, Fort Lauderdale and surrounding area residents will find all types of health and wellness related information including information about our office and services, chiropractic, back and neck pain, work and auto injuries and much, much more.
We hope you enjoy the information within and share it with others. Again, thank you for visiting www.drwassermann.com and remember to come back often.
Dr. Jonah A. Wassermann, D.C.


text:
Chyasal ANFA Technical Football Center – to stadion piłkarski w Lalitpurze w Nepalu. Jest własnością All Nepal Football Association (ANFA) i swoje mecze rozgrywają na nim drużyny piłkarskie Nepal National League. Stadion może pomieścić 3000 widzów.

Bibliografia

 Informacje o stadionie na wikimapia

Stadiony piłkarskie w Nepalu

redpajama_set_name: RedPajamaWikipedia


text:
Q: How can I keep  &  html tags in my coldfusion string but get rid of all other html tags? How can I keep whatever & whatever html tags in my coldfusion string but get rid of all other html tags?

A: Although there are many ways to run regex magic in CF, I still prefer some Java here to walk through content and capture stuff.

<cfsavecontent variable="stringToStrip">

        Hello World

foo

        bar

</cfsavecontent>

<cfset stripRegEx = "<[^>]+>">

<cfset result   = createObject("java", "java.lang.StringBuilder").init()>
<cfset matcher  = createObject("java", "java.util.regex.Pattern").compile(stripRegEx).matcher(stringToStrip)>
<cfset last     = 0>

<cfloop condition="matcher.find()">

    <cfset result.append(
        stringToStrip.substring(
            last,
            matcher.start()
        )
    )>

    <cfset capture = matcher.group(
        javaCast("int", 0)
    )>

    <cfif reFindNoCase("</?su[bp]", capture)>
        <cfset result.append(capture)>
    </cfif>

    <cfset last = matcher.end()>
</cfloop>

<cfset result.append(
    stringToStrip.substring(last)
)>

<cfset result = result.toString()>

<cfoutput>#result#</cfoutput>

Output is:
Hello World
foo
bar

A: I think you could also use a negative lookahead in a regex replace like so:
stripped_string = reReplaceNoCase(source_string, '<(?!/?su[bp]\b)[^>]+>', '', 'all' );


This week I travelled to the island of Ibiza together with my other half so I thought it would be nice to share a few places I (re)discovered during our stay on this tiny slice of paradise with you all, in case anyone thought about making a similar trip (which I can highly recommend!). There are so many incredible spots worth visiting and to be honest I'm quite far away from knowing everything but here are a few of the pretty gems I came across.
This has become my new favorite place on Ibiza. We enjoyed having breakfast here (the pancakes with blueberries and the homemade organic crunchy granola are definitely worth trying) and the atmosphere is chic but casual and inviting. Serving classic and simple dishes to both locals as well as tourists who managed to find their way here. The perfect place to spend our first morning on the island.
Adorable place, delicious food and a lovely atmosphere, that's how I'd describe this incredible place. Grab a table and enjoy a long lazy lunch overlooking the ocean and the beach. Relax on a warm summer's day by the beach, have a glass of rosé and take in the pretty view.
The sister venue to Amante, Aiyanna is one of the most idyllic spots on the island. We started our morning here practicing yoga with ocean views, and enjoyed a healthy breakfast on the cozy terrace afterwards. Located on the stunning Cala Nova bay, it's the perfect place to spend a day on the beach.
If you're looking for a bit of Italian charm on Ibiza, then I can highly recommend visiting It restaurant. They simply have the best Mediterranean cuisine and a lovely atmosphere overlooking the harbour. The chocolate mousse, raspberry and walnut crumble is simply divine.
If you are interested in more Ibiza tips from my first visit to this island, you can find them here.


Hrm it's been awhile since I've said anything on this blog. I was in Calgary for a bit and just have been keeping pretty quiet these days.
This is at the top of a mountain where you take a terrifyingly long (and expensive) Gondola ride to get to.
We went to the show Sean helped put on, "The Chute Project" (which I did the poster for) at Straw Gallery. It was pretty great by the way, and congrats to Sean and everyone else who helped put it on.
The Backside of Your Gullet is Decadent and Depraved by Frank Chimero - It's a four part (don't let this deter you, this is really really good), series of little, very easy to read essays basically about, what is the point of doing anything (something that I, and probably everyone else thinks about everyday from everything to the smallest to largest thing). Chimero is a great designer, has great clients, but he feels a little lost, stuff's feeling a bit pointless (I feel ya buddy). And I think it's great that he's admitting that and trying to figure out why and sharing his experience along the way (because I'd sure like some light shed on this as well).
But Will it Make You Happy? - This New York Times article really affected me, making me reevaluate how I live and how to live, trying to make things slower and simpler. This includes checking my email, facebook, and twitter less, which so far I feel like I've done in an okay and somewhat improved way, and ha, I hope it lasts longer than this week. I always do this detox sort of thing for a week and then eventually just give it up because I don't know how to occupy my time! But I guess that's the point.


This is a great opportunity for a Business student to gain an amazing all-round experience with this entrepreneurial host who supplies over 10,000 British branded consumer products to ex patriots worldwide to give them that home from home feeling. Mentored throughout, you will assist in the marketing, buying, logistics and all kinds of administration duties in this fast-moving environment. For the selected candidate this will no doubt be a challenging but equally rewarding role both personally and professionally.
Are you a registered student? Or Are you eligible to participate in the Erasmus+ programme?
See website for details of all ESPA benefits. For all internships over 6 months, additional benefits will be paid. Details available at interview.


5 December 2011 - Yokohama HPT racing through stock checks with Datalogic's Skorpio™.
Sanderson rolls out Datalogic Mobile's Kyman™ at Lakeland.
The retail chain Fussl invests in store logistics with the Skorpio™ mobile computer from Datalogic Mobile.
The confectionery expert, Schokothek, uses the Memor™ mobile computer for its inventory.


The DaVinci Group offers a unique funding model by issuing ownership shares in the company to 350 integrators and about 10 reps, who each pay annual $2,000 fee to carry the line.
Back on March 29, 1984, the Baltimore Colts football team sneakily packed its trucks and left in the middle of the night. The city and fans were ignominiously left with nothing, while the wealthy owners moved to Indianapolis to count their new-found riches.
On the flip side of that story is the Green Bay Packers. Since 1923, the team itself has been owned by its fans, with public shares offered for sale every so often.
The DaVinci Group, makers of TDG Audio, Blue Aura and Vanguard Dynamics audio brands of in-ceiling speakers and multichannel amps, might be the Green Bay Packers of the custom electronics industry. When industry veterans Dan Kippycash and Alex Chiou created the company earlier in 2013, the duo decided it wanted to create a company that shared the wealth. As such, the company's unique funding strategy includes providing ownership shares to dealers who buy and install the lines, and to reps that carry it.
With that as a backdrop, the two former SpeakerCraft execs decided to offer reps, dealers and employees of DaVinci Group ownership shares.
The original plan was to cap the number of integrators at 700, with each getting shares of the company. But several large dealers have already joined on as resellers, so the new plan calls for only 350 dealers with shares and another 350 non-shareholder dealers. It also calls for about 10-11 rep firms. There is no minimum-size dealer.
Here's how it works: The 350 dealers and the reps must pay an annual fee of $2,000 to become a DaVinci integrator. For that commitment, they not only get ownership shares, but lifetime free shipping, co-op marketing, free demo product and an annual profit-share outlay. If the integrator purchases $25,000 or more in product in any given calendar year, his annual $2,000 fee is waived.
By capping the total number of dealers, it puts pressure on Kippycash and Chiou to select the "right" dealers and reps. To date, they have 150 dealers on board and three reps. The cumulative shares owned by the dealers and reps will equate to a 20 percent stake in the company.
There is one big difference between DaVinci and the Green Bay Packers: the Packers are a non-profit while DaVinci is not. But does this mean Kippycash and Chiou are Vince Lombardi and Brett Favre?


Not only as a player, Rishabh Pant has matured tremendously as a person in the last one year and the knock against Mumbai Indians is one of the many he will play in coming years, said Delhi Capitals coach Ricky Ponting.
New Delhi: Not only as a player, Rishabh Pant has matured tremendously as a person in the last one year and the knock against Mumbai Indians is one of the many he will play in coming years, said Delhi Capitals coach Ricky Ponting.
Pant, Daredevils' top scorer last season, has set the tone with a blistering 78 off 27 balls against Mumbai Indians during his team's 37-run win in Mumbai on Sunday.
"I have seen Rishabh mature in his game and as a person and that won't be the only game he wins for DC. He will win many more games for sure," a proud coach said on the eve of Delhi's match against Chennai Super Kings in Delhi.
Terming Rishabh as a finisher, Ponting feels that he is at his best whenever a platform is created.
"If you look at the back half of the last edition, he was playing his best when our team was playing its best in those games. Even yesterday, we set up a good platform for him to go and express himself.
"That is what we want from Shikhar. Hold the top-order together. Probably, the reason he was more subdued was because it wasn't easy to bat early on. First three or four batsmen told me that there was a bit of seam movement.
"We needed Shikhar to play that sort of role and get through the tough overs and that was going to allow Ingram and Rishabh to play the way they did later on. It's been a really encouraging start for Shikhar.


White House aide Kelly Sadler promised Meghan McCain she would apologize publicly for mocking her father's health, a source familiar with the conversation has told CNN, but Sadler has yet to do so.
Sadler, a special assistant for surrogate communications, made the promise in an apologetic phone call to McCain after it was reported that she had jokingly said of Sen. John McCain, "he's dying anyway," in response to his opposition to President Donald Trump's pick for CIA director, Gina Haspel. The Republican senator is currently at home in Arizona as he battles brain cancer after receiving a diagnosis last year.
During the phone call, McCain told Sadler she needed to apologize publicly, and the White House aide said she would, according to the source. As of Sunday, however, neither the White House nor Sadler has made a public apology.
Sadler did not immediately return a request for comment Sunday.
On Friday, Meghan McCain responded to the reports on Sadler's remarks, which she made Thursday in a private meeting, by questioning how the White House aide could still be employed.
"I don't understand what kind of environment you're working in when that would be acceptable and then you can come to work the next day and still have a job," she said on ABC's "The View."
A White House official told CNN on Thursday that Sadler meant the comment as joke, "but it fell flat."
The White House has not denounced the remark, which has drawn condemnation from prominent political figures, including Republican Sen. Lindsey Graham of South Carolina, who is a close friend of McCain's, and former Democratic Vice President Joe Biden.
White House press secretary Sarah Sanders privately admonished communications and press staff on Friday over the leaked comment, according to several sources familiar with the meeting. But Sanders has focused more on the leaking of the comment than its substance, according to the sources.


Lately I have been having very vivid, realistic dreams. Realistic in how they feel, not the actual content, seeing how I doubt I am going to accidentally drive a bus over a cliff into a giant, swelling wave on the west coast of Canada or that I would need a clown to rescue me when I fall into the street and then joyfully parade me down the sidewalk. But these dreams, even with unrealistic events- they stick to my brain as if they were real. As if they were important.
I don't feel as if dreams are some foreboding of possible futures. I don't think they are meant to be or that they should be taken as serious life advice. For the most part I get that dreams are just the way our stressed minds piece together fragments of emotion and memory to allow us to process and let go of things we don't need to hold onto. For the most part.
But I also like to believe that dreams are something more. When I have a dream that features someone that I know, I like to believe that it is somehow significant. That it is my spirit's way of stretching out into the void and searching for them and maybe, just maybe, finding them for a momentary embrace. Sometimes it reminds me to call or write to someone, but most of the time it just reminds me of their significance.
Back when I used to contemplate on souls for a large portion of my day, I had a theory about how souls were made. I didn't think that we had a single soul mate, but many people who come across our lives that have pieces of soul that "fit" us. When we have a meaningful connection- a long friendship, a lover, or even just a single serving friend that makes us stop and thing- we trade pieces of our souls. You recognize something in me, and I recognize something in you, and we rotate around each other until we get- through force, coercion, or understanding- that piece of soul that "belongs" to us. When we die, our soul breaks apart. All of the little pieces that still don't fit shatter off, but there is a larger piece that stays whole. The piece that will begin collecting new pieces during our next life.
I wonder if when I dream and can feel the person I am dreaming about, whether that might be a chip of their soul that I have taken into myself waking up, glowing, reminding me of the person from which it came. That they are now inside of me and I am inside of them. Or maybe it is stretching little tendrils out, looking for the body that it had grown up with, knowing that it belongs to me but still missing the person that it lived within for years.