text:
Antelope Island in the Great Salt Lake
After visiting Hardware Ranch and eating at Maddox Drive-Inn in Brigham City we drove to Syracuse, Utah, and then west over the seven mile long causeway to Antelope Island. Antelope Island is the largest of 10 islands in the Great Salt Lake. It is 42 square miles, 15 miles long at the longest points and 5 miles wide at the widest points.
It was named by John C. Fremont and Kit Carson, who visited the island in 1845, after a pronghorn antelope they shot on the island for food. Bison were introduced in 1893 and the Antelope Island herd has proved to be a great genetic pool for bison preservation in the U.S.
The Wasatch Front as viewed from Antelope island.
I've been to Antelope Island a number of times, including several times in the last ten years, but I've never seen an antelope there. So this time, as I entered with my two oldest grandgirls, I asked the ranger (it is a State Park) where the best place to see antelope was. I was told they could be seen on the flats on the east side of the island on the drive to Fielding Garr Ranch.
The eastern shore with the Wasatch Front in the backgrounds.
Frary Peak, part of the mountainous backbone of the island.
Just past the causeway we turned left and saw some mule deer feeding off the right side of the road. We paused and got some pictures.
Then we drove for a while and saw some pronghorn antelope off to the left at some distance. We got out and took some photos. Fortunately I had my 500 mm lens, but even with it they appeared quite small. But at least now I've seen antelope on Antelope Island.
Pronghorn antelope, the Great Salt Lake, and the Wasatch Front toward the back.
We drove further and saw quite a few different groups of bison. Some of them were a great distance up on the side of the mountain and some were a great distance actually wading through water to a small island off the island. But one group, in particular, was quite near the road and we got some good views. The grass is still quite tall and they keep their heads down grazing. So it is difficult getting good photos with head shots.
This big, beautiful male, was staring right at us.
We drove as far as Fielding Garr Ranch where the road ends and turned around and headed back.
It was quite a day. We saw mule deer in multiple places, wild turkey in multiple places, elk, pronghorn antelope and bison.
To top off the evening we stopped at Smith's in Lehi, on the way back home, and in keeping with tradition (for example, see here), we prepared a seafood dinner. We got a good deal on some small octopi, five pounds of Alaskan king crab legs and a one pound lobster tail. We fried the octopi in butter and they weren't bad. And of course, king crab and lobster are always good.
Small, uncooked octopi ready to be cooked.
A cooked octopus on a Christmas plate.
Posted by Bob at 7:00 AM 1 comment:
Maddox Drive-Inn - Brigham City, Utah
I previously posted a visit to Hardware Ranch on my day excursion with my two oldest grandgirls. We'd taken sleds driven by horses out to see wild elk and were on our way to Antelope Island in the Great Salt Lake. Maddox Drive-Inn, in Brigham City, was on our way and has been an institution since I was a child. We used to eat there when we went boating on Willard Bay or were driving through on our way to Logan or Idaho. I remember it as a place for hamburgers and fried chicken.
I asked my grandgirls if they'd ever been to a drive-in where you ordered from your car and had a person deliver the food to your window. They immediately mentioned Sonic Drive-Inn. Growing up, I only knew of Maddox and Hire's in Salt Lake City. That was part of what made it fun.
It was cold, in the 20s, so the drive-inn concept was a bit chilly. We kept the car running and the heater going.
I also ordered a home brewed root beer and enjoyed it. Less fizzy and more mellow.
Maddox is known for its fried chicken, which is skinless, a recipe developed by I.B. Maddox in 1949. The flour must have corn meal in it. We ordered a chicken basket and asked for all thighs and got four of them. The thighs came with a home-made roll, some raspberry butter and a scone. I ate one thigh, as did one of my granddaughters, and the other granddaughter had the other two thighs and the roll and scone. She really liked the roll and scone, I didn't try them. I was really surprised by the chicken. I hadn't remembered it was skinless, which totally changes the taste and texture. It was relatively moist, because it was a thigh, but I would not order a breast, confirmed by other reviews I've seen. I think it would be dreadfully dry. It was fun to try again, but I'll pass on it in the future.
I ordered a buffalo burger. I was thinking of Antelope Island and all of the local bison, figuring it might be fresh and local. It came with a squared Wendy-like patty on a bun with a little bit of lettuce, a few pickles, a mixture of ketchup and mustard and a small slice of cheese. It was a big disappointment, a view shared by a burger connoisseur I found on-line. I didn't like the combination of condiments and the burger was quite dry and dominated by bun.
Each order came with fries or onion rings. I got the onion rings and special orders of fry sauce which are $.25 extra. The onion rings are all breading and no onion.
My other granddaughter got a cheese burger and fries. She shared some fries and they were very good: thick and salty.
All in all, I would probably only give it a 3 on a scale of 5, yet it remains a place that I and others talk about when Brigham City is mentioned. Although I have no desire to go back, if I am ever passing through Brigham City again and I'm hungry, I'll probably stop there, and I'll probably order some fried chicken and complain that it is not as good as a remember it. It is just one of those things: a fond childhood memory that I have to keep going back to.
At any rate, it was fun to experience it again with my grandgirls. On the way out of town, on our way to Antelope Island, we saw a huge group of elk in a field and on the slopes of a mountain, also being fed hay. It looked like there were as many or more than we'd seen at Hardware Ranch.
Elk at White Peaks Ranch, just off the side of the road.
I just Googled it and found that we'd seen a private elk herd owned by White Peaks Ranch, which has 800 head of elk. They focus on bull elk and sell the antlers in Eastern Asia where they are ground up and used for medicinal purposes, such as an aphrodisiac. They also sell private, guided and guaranteed elk hunts. I was recently talking to the supplier of exotic meats I frequent and he mentioned he bought his elk meat from a ranch in Utah. This could be the place, although there are other private elk ranches.
Hardware Ranch Elk - Utah
Over Christmas break I visited Utah and took my two oldest grandgirls (ages 8 and 11) on a day-long outing. Our first visit was to Hardware Ranch, a Wildlife Management Area owned and managed by the State of Utah in Blacksmith Fork Canyon, 15 miles east of Hyrum (which is 8 miles south of Logan) on Hwy 101. The journey was about 120 miles from my daughter's home in Lehi (up the I-15 to Brigham City, then up Hwy 89 to about Wellsville, then east on Hwy 101).
I was using Google Maps on my phone and ended up following a much more interesting route which supposedly saved 8 minutes. That route took us off the I-15 in about Roy, then up Hwy 80 into Ogden, then east up Ogden Canyon on Hwy 39, past Pineview Reservoir, past the turn-off to Causey Reservoir, then branching off on to a very long snow covered dirt road for about 20 miles. Google Maps told us we'd arrived while we were in the middle of nowhere (it must have put us at the edge of the Hardware Ranch property). Fortunately, we were able to talk to some snowmobilers who told us to follow the road for many more miles down into the bottom of the canyon (we followed the conventional route on the way back to Lehi).
On the way down the canyon the girls, with their eagle eyes, spotted some mule deer on the side of a hill and we took a few pictures.
We eventually found the Hardware Ranch Visitor's Center which had a large stuffed bull elk, a stuffed mountain lion and bobcat, some pelts, including mountain lion, fox, coyote, bobcat and badger, and paid for the sled ride, a very reasonable $5.00 for me, and $3.00 for each of my grandgirls.
Our dirt road connected with Hwy 101 at this spot at Hardware Ranch.
The sleds are large rectangular beds with seats around the edges, mounted on truck tires and pulled by two large horses. The elk are fed hay, from December through February, to keep them from going into the more populated areas to forage and get into trouble. The elk are completely free to come and go and the larger bull elk come a little later in the winter when the snow gets deeper.
We went out on a sled, with about 10 or 15 other people, and spent about 20 or 30 minutes stopping four or five times. There were two or three other sleds out at the same time as we were. There were lots of elk, mostly females and younger spike bulls, totaling about 400.
We never got close enough to be able to reach out and touch them, but close enough to get a very good look at them. We were also nearby a grouping when one of them let out its haunting bugling call. It was magnificent.
My grandgirls at the back of the sled.
There were a couple of good-sized bulls.
This guy was a real beauty.
A number of spike bulls with long thin "spike" antlers.
And we saw a couple of young elk rear up on their hind legs and strike at each other with their front legs.
And an older calf aggressively going after it's mother's milk.
The temperature was in the low 20s, but we were bundled up pretty good and were not too cold.
At the end, the grandgirls were able to pet the snouts of our sled horses which I think they enjoyed as much as the elk.
After leaving the Ranch and starting down Blacksmith Fork Canyon toward Hyrum, we saw a group of about 20 Rio Grande turkeys on the side of the hill just off the road.
We continued on to Brigham City where we ate at Maddox Drive-Inn, then continued on to Antelope Island in the Great Salt Lake where we saw some antelope and bison. Those are subjects for my next two posts.
Gur Emir Mausoleum of Timur - Samarkand
Guri Amir, also known as Gur-e-Amir, Gur-i-Amir and Gur Emir, is the mausoleum of Timur, also known as Tamerlane, located in Samarkand, Uzbekistan. Timur lived from 1336 to 1405 and had a huge empire which stretched from India to the Mediterranean. He made Samarkand his capital.
Painting of Timur in the mausoleum.
The mausoleum was very important architecturally and the precursor and model for later mausoleums, including the Taj Mahal. The name is Persian for "Tomb of the King." It contains the tombs of Timur, his sons Shah Rukh and Miran Shah, and grandsons Ulug Bek (aka Ulug Begh) and Muhammad Sultan.
Statue of Timur's grandson, Ulug Bek.
It also contains the tomb of Timur's teacher, Sayyid Baraka.
The archaeological remains in the foreground are the madrasa and khanaka which have not been restored.
The entrance portal.
Detail on the upper entrance portal.
View of the mausoleum through the entrance.
The complex was originally built by Muhammad Sultan as a center for Islamic education. It included a madrasa, to teach the children of Samarkand's nobility; and a khanaka or khanqah, a spiritual retreat for Sufis. After the death of Muhammad Sultan in 1403, Timur's heir apparent, Timur had his grandson's body temporarily placed in a room of the madrasa and ordered construction of the mausoleum on the site.
The mausoleum had an entrance portal, a single decorative wall, and four minarets, one at each corner.
Back of the mausoleum.
Timur did not live to see the mausoleum completed. He died in 1405 while on a military campaign in China. Timur had already built a smaller tomb for himself in Shahrisabz, near his Ak Saray Palace. However, the passes to Shahrisabz were snowed in and so Timur was buried in this mausoleum in Samarkand instead. His body was perfumed with rosewater, musk and camphor and put in a coffin decorated with precious stones. Ulug Bek, another grandson, completed the mausoleum and it became the family crypt for the Timurid Dynasty. Ulug Bek is also buried there.
The exterior consists of blue, light blue and white tiles organized into geometric and epigraphic (inscriptions as writing) ornaments on a background of terracotta bricks. The bright blue dome is heavily ribbed.
Inside, the mausoleum has a high chamber with deep niches. The lower walls are covered with onyx slabs decorated with paintings. Large parts of the walls are covered with painted plaster and ornamented by high-relief papier-mache cartouches (a carved tablet or drawing representing a scroll), gilded and painted. The inside of the dome is gilded with six and a half pounds of gold.
Inside the dome - sparkling with gold.
Amazingly gold.
Lower wall panels are made of onyx.
The carved fake sarcophagi indicate the location of actual tombs underneath. Ulug Bek had a solid block of dark green jade put over Timur's tomb, the largest known specimen of jade in existence. Soviet scientists opened Timur's tomb in June 1941 and confirmed that he was tall, just under five feet eight inches, and was wounded in the leg and arm and would have walked with a pronounced limp.
Various tomb markers. Timur's is the green jade.
Timur's green jade marker (above his tomb).
Timur's mausoleum has been restored, but the madrasa and khanaka are still in ruins.
Local women visiting the mausoleum.
Posted by Bob at 10:27 PM 1 comment:
Necropolis of Shah-i-Zinda - Samarkand
Water Buffalo Tenderloin, Striploin and Ribeye - S...
Post-Thanksgiving Wild Turkey
Katta Langar Mosque - Uzbekistan


Hey there guys! Grace here! I was just looking through my pictures and saw some from the spring. So, I decided to share them with you! My friend had come over and we went for a walk and a picnic.
Did you guys like it? I hope so!
Are you guys looking forward to school starting? I am! It should be a fun year. :) Well, see you later!
So pretty! What doll is with Grace?
Hey thanks! Um, that is my friend's doll, Carrie!


text:
Q: GUI Pattern Question: PropertyChangeListener vs. Specialized View Interface I'd like to pair a Model with it's View through an interface. I want to control when and how often the view is updated. So something like PropertyChangeListener wouldn't work well (where an event is fired after each property is set).
I'm not developing for a specific GUI framework. The goal here is the ability to swap out different GUI front ends (right now for testing, but might be useful later for different versions of the app). These might be Swing, or it might be a web browser (via GWT, for example).
Below is my approach. The view implements an interface to provide a method to update. This is triggered by the controller when it determines it's done updating the model. This still feels ok to me, since the Controller is only interacting with the view through the model, the controller is not dependent on a particular implementation of the View.
So, I guess my question(s) are

*

*does this work well?

*Is this standard practice?

*Does this pattern have a name?

Rough code sample (Java):
// Controller, manages Items (the model)
class ItemList {

   void addItem(Item item) {
   }

   void doStuffWithItems() {

     // perform some set of operations, such as sorting or layout
     for (Item item : items) {
       // ....
     }

     // now with everything in it's final position:
     for (Item item : items) {
       item.updateView();
     }
   }
}

// Model
class Item {
  private int top;
  private int left;
  private int width;
  private int height;

  // Can remember it's previous position/size:
  public void savePostion() {
  }

  // And recall it for the Controller to use:
  public public Position getSavedPosition() {
  }

  // Plus some other useful functions:
  public boolean intersectsWith(Item other) {

  }

  public void updateView() {
    this.view.update();
  }

  void setView(ItemView view) {
    this.view = view;
  }
}

// Interface used by View implementations
public interface ItemView {
  // Trigger the view to reflect the current state of the model
  void update();
}

// Example, as a Swing component
class ItemComponent extends JComponent implements ItemView {
  private Item item;

  public ItemComponent(Item item) {
    this.item = item;
    item.setView(this);
  }

  // ItemView#update
  public void update() {
    // update the component's size/position
    setBounds(new Rectangle(item.getLeft(), item.getTop(), item.getWidth(), item.getHeight()));
  }

  @Override
  public void paint(Graphics g) {
    // ...
  }
}

A: I would avoid forcing the View to implement an interface only for change notification. Create a separate "update now" event on the model instead.

A: The model should not be controlling or know about the view directly.  The view should register a callback with the controller so the controller can tell the view when to update, that's why its the controller.  You could have the model allow external listeners for a modelChangedEvent.  Then the view could register with the model in that respect without the model knowing there was a view. See the J2EE blueprint for MVC and how there is an indirect event notification of state change in the model.

A: For traditional applications that run on the desktop of a computer I recommend variants of the Passive View. The class responsible for creating and managing the form is a thin shell that passes events to the UI Object. The UI_Object interact with the form via a interface. In term the UI Object implements a UI_View Interface and registers itself with a View Controller that is situated lower in the object hierarchy.
The UI_Object then execute object implementing the Command Pattern which modifies the model. The command object can interacts with the various views via the interfaces exposed by the View Control.
What this does is allow you to rip off the form classes and replace them with stub classes that implement the form interfaces. The stub classes are used for automated testing especially for integration tests.
The interfaces precisely define the interaction between the Form, the UI_Object, Commands and the views. They can be designed to be relatively language agnostic so they can make porting between platform easier.
What you are missing in your example are command objects. You need this structure

*

*ItemViewForms

*ItemViewImplementation

*ItemViewFormInterface

*ItemViewCommands

*ItemViewInterface

*MyModel

Incorporate ItemList in the ItemViewImplementation
ItemComponent would register with the ItemViewImplementation using the ItemViewInterface.
The sequence of events would look something like this

*

*User wants to update the Item

*Clicks on the UI (assuming that UI
involves clicking with a mouse)

*The Form tells the
ItemViewImplementation through the
ItemViewInterface that X has been
done with Y parameters.

*The ItemViewImplementation then
creates a command object with the
parameters it needs from Y.

*The Command Object take the Y
Parameters modifies the model and
then tells the

*ItemViewImplementation through the
ItemViewInterface to update the UI.

*The ItemViewImplementation tells the
ItemViewForms to update the UI
through the ItemViewFormInterface.

*The ItemViewForms updates.

The advantage of this approach is that the interaction of each layer is precisely defined through interfaces. The Software ACTIONS are localized into the command objects. The Form layers is focused on display the result. The View layer is responsible for routing actions and response between the Commands and Forms. The Commands are the only things modifying the model. Also you have the advantage of ripping off the Forms Implementation to substitute any UI you want including mock object for unit testing.

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}\label{sec:intro}

A taxonomy is a semantic hierarchy of words or concepts organized \emph{w.r.t.}\xspace their \emph{hypernymy} (\emph{a.k.a.}\xspace \texttt{is-a}\xspace) relationships.
Being a well-structured resource of lexical knowledge, taxonomies are vital to various tasks such as question answering~\cite{C18-1042}, textual entailment~\cite{dagan2013recognizing,bowman2015large,yu2020enriching}, and text generation~\cite{biran2013classifying}.
When automatically building taxonomies from scratch or populating manually crafted ones, the \emph{hypernymy detection} task plays a central role.
For a pair of queried words $(x_q,y_q)$, hypernymy detection requires inferring the existence of a hyponym-hypernym relationship between $x_q$ and $y_q$.
Due to the good coverage and availability, free-text corpora are widely used to facilitate hypernymy detection, resulting in two lines of approaches: \emph{pattern-based} and \emph{distributional}.

\begin{figure}[t]
  \centering
  \includegraphics[width=1.0\columnwidth]{fig/emnlp_framework_new.pdf}
  \caption{The overall framework of complementary methods for hypernymy detection from corpus. Different sparsity types of queried pairs are handled with pattern-based and distributional models respectively. }\label{fig:emnlp_framework}
\end{figure}

Pattern-based approaches employ pattern pairs $(x,y)$ extracted via \emph{Hearst-like patterns}~\cite{hearst1992automatic}, \emph{e.g.,}\xspace ``$y$ \emph{such as}
$x$'' and ``$x$ \emph{and other} $y$''.
An example of extracted pattern pairs from corpus are shown in Figure~\ref{fig:emnlp_framework}.
Despite their high precision, the extracted pairs suffer from sparsity which comes in two folds \emph{i.e.,}\xspace \textbf{Type-\RN{1}}: $x_q$ and $y_q$ separately appear in some extracted pairs, but the pair $(x_q,y_q)$ is absent \emph{e.g.,}\xspace (dog, animal); or
\textbf{Type-\RN{2}}: either $x_q$ or $y_q$ is not involved in any extracted pair \emph{e.g.,}\xspace (crocodile, animal).

Although matrix factorization~\cite{P18-2057} or embedding techniques~\cite{vendrov2016order,nickel2017poincare,le2019inferring} are widely adopted to implement pattern-based approaches, they only relieve the \textbf{Type-\RN{1}} sparsity and cannot generalize to unseen words appearing in the \textbf{Type-\RN{2}} pairs.
On the other hand, distributional ones follow, or are inspired by, the \emph{Distributional Inclusion Hypothesis} (DIH; \citealt{geffet2005distributional}), \emph{i.e.,}\xspace the set of the hyponym's contexts should be roughly contained by the hypernym's.
Although applicable to any word in a corpus, they are suggested to be inferior to pattern-based ones fed with sufficient extracted pairs~\cite{P18-2057,le2019inferring}.

Since pattern-based methods have unresolved sparsity issues, while distributional ones are more broadly applicable but globally inferior, neither of them can dominate the other in every aspect.
In this light, we are interested in two questions:
\begin{itemize}
\vspace{-0.5em}
\item Is the \textbf{Type-\RN{2}} sparsity severe in practice?
\vspace{-0.5em}
\item If so, how to complement pattern-based approaches with distributional ones where the former is invalid?
\end{itemize}
\vspace{-0.5em}

To answer the first question, we conduct analyses involving estimations on real-world corpora as well as statistics of common hypernymy detection datasets.
Results from both resources indicate that the likelihood of encountering the \textbf{Type-\RN{2}} sparsity in practice could even reach up to more than 50\%, which is thus non-negligible.

For the second question, we present ComHyper, a complementary framework~(Sec.~\ref{sec:4.1framework}) which takes advantage of both pattern-based models' superior performance on \textbf{Type-\RN{1}} cases and the broad coverage of distributional models on \textbf{Type-\RN{2}} ones.
Specifically, to deal with \textbf{Type-\RN{2}} sparsity, instead of directly using unsupervised distributional models, ComHyper uses a training stage~(Sec.~\ref{sec:4.3training}) to sample from output space of a pattern-based model to train another supervised distribution model implemented by different context encoders~(Sec.~\ref{sec:4.2encoder}).
In the inference stage, ComHyper uses the two models to separately handle the type of sparsity they are good at, as illustrated in Figure~\ref{fig:emnlp_framework}.
In this manner, ComHyper relies on the partial use of pattern-based models on \textbf{Type-\RN{1}} sparsity to secure performance no lower than distributional ones, and further attempts to lift the performance by fixing the former's blind spots (\textbf{Type-\RN{2}} sparsity) with the latter.
On several benchmarks and evaluation settings, the distributional model in ComHyper proves effective on its targeted cases, making our complementary approach outperform a competitive class of pattern-based baselines~\cite{P18-2057}.
Further analysis also suggests that ComHyper is robust when facing different mixtures of \textbf{Type-\RN{1}} and \textbf{-\RN{2}} sparsity.

Our contributions are summarized as :
\textbf{1)} We confirm that a specific type of sparsity issue of current pattern-based approaches is non-negligible.
\textbf{2)} We propose a framework of complementing pattern-based approaches with distributional models where the former is invalid.
\textbf{3)} We systematically conduct comparisons on several common datasets, validating the superiority of our framework.

\section{Related Work}\label{sec:relatedWork}

\paratitle{Pattern-Based Approaches.}
Taxonomies from experts (\emph{e.g.,}\xspace WordNet~\cite{miller1995wordnet}) have proved effective in various reasoning applications~\cite{song2011short,DBLP:conf/acl/ZhangZS20}.
Meanwhile, Hearst patterns~\cite{hearst1992automatic} make large corpora a good resource of explicit \texttt{is-a}\xspace pairs, resulting in automatically built hypernymy knowledge bases~\cite{wu2012probase,seitner2016large} of large scales.
The coverage of both words and hypernymy pairs in those resources are far from complete.

To infer unknown hypernymies between known words, \emph{e.g.,}\xspace implicit \texttt{is-a}\xspace pairs in transitive closures, pattern-based models are proposed.
\citet{P18-2057} and \citet{le2019inferring} show that, on a broad range of benchmarks, simple matrix decomposition or embeddings on pattern-based word co-occurrence statistics provide robust performance.
On Probase~\cite{wu2012probase} - a Hearst-pattern-based taxonomy, \citet{yu2015learning} use embeddings to address the same sparsity problem.
Some methods~\cite{vendrov2016order,athiwilson_doe_2018,nickel2017poincare,nickel2018learning,pmlr-v80-ganea18a} embed WordNet in low-dimensional space.
Depending on vectors of words learnt from known \texttt{is-a}\xspace pairs, the above pattern-based methods cannot induce more hypernymy pairs whose words do not appear in any pattern.

\paratitle{Distributional Approaches.}
Distributional models are inspired by DIH~\cite{geffet2005distributional}.
They work on only word contexts rather than extracted pairs, thus are applicable to any word in a corpus.
Early unsupervised models typically propose asymmetric similarity metrics over manual word feature vectors for entailment~\cite{weeds2004characterising,clarke2009context,santus2014chasing}.
In \citet{chang2018distributional} and \citet{NguyenEtAl:17b}, the authors inject DIH into unsupervised embedding models to yield latent feature vectors with hypernymy information.
Those feature vectors, manual or latent, may serve in unsupervised asymmetric metrics or to train supervised hypernymy classifiers.
\citet{shwartz2017hypernyms} explore combinations of manual features and (un)supervised predictors, and suggest that unsupervised metrics are more robust \emph{w.r.t.}\xspace the distribution change of training instances.
Projection learning~\cite{fu-etal-2014-learning,ustalov-etal-2017-negative,wang-he-2020-birre} has been used for supervised hypernymy detection.

\paratitle{Other Improved Methods.}
Due to weak generalization ability of Hearst patterns, \citet{anh2016learning} and \citet{shwartz-goldberg-dagan:2016:P16-1} relieve the constraints from strict Hearst patterns to co-occurring contexts or lexico-syntactic paths between two words.
They encode the co-occurring contexts or paths using word vectors to train hypernymy embeddings or classifiers.
Although leading to better recall than Hearst patterns~\cite{washio-kato-2018-filling}, they limit the trained embeddings or models from generalizing to every word in a corpus.
Nevertheless they have no ability to cope with the \textbf{Type-\RN{2}} sparsity, which is the main focus of our work.

Another line of retrofitting methods~\cite{vulic2018post,N18-1103},  \emph{i.e.,}\xspace adjusting distributional vectors to satisfy external linguistic constraints, has been applied to hypernymy detection.
However, they strictly require more additional resources \emph{e.g.,}\xspace synonym and antonym to achieve better performance~\cite{kamath-etal-2019-specializing}.
To the best of our knowledge, we are the first to propose complementing the two lines of approaches to cover every word in a simple yet efficient way, with extensive analysis of the framework's potential and evaluation of performances.

\section{Preliminaries}\label{sec:preliminaries}

We formally define the aforementioned two types of sparsity,
and provide some statistical insights about their impacts on pattern-based methods.

\subsection{Notations and Definitions}

Let $V$ be the vocabulary of a corpus $\mathcal{C}$.
By applying Hearst patterns on $\mathcal{C}$, a set of \emph{extracted pairs} $\mathcal{P}\subseteq V\times V$, \emph{i.e.,}\xspace \texttt{is-a}\xspace relationships $\{(x,y)\}$ ($x,y\in V$), is obtained.
As in Section~\ref{sec:relatedWork}, pattern-based approaches usually use $\mathcal{P}$ to perform matrix factorization or embedding learning.
Due to their nature, only words ``seen'' in $\mathcal{P}$, or $V_\mathcal{P}=\{x\mid(x,y)\in \mathcal{P}\vee (y,x)\in\mathcal{P}\}$, will have respective columns/rows or embeddings.
We refer to them by \emph{in-pattern} (or IP for short) words.
We refer to words without columns/rows or embeddings, \emph{i.e.,}\xspace $V\setminus V_\mathcal{P}$, by \emph{out-of-pattern} (or OOP) words.

Suppose a pair of words $q=(x_q,y_q)$ is queried for potential hypernymy.
We say $q$ is an IP pair if both $x_p$ and $y_p$ are IP words, or an OOP pair if either of them is OOP.
Due to the need of explicit columns/rows or embeddings for both $x_q$ and $y_q$, pattern-based approaches may only make inferences on IP pairs, but are infeasible on OOP ones.

\subsection{Observations and Motivation}

Given the infeasibility of pattern-based methods on OOP pairs, we are interested in what extent pattern-based methods are limited, \emph{i.e.,}\xspace the rough likelihood of encountering OOP pairs in practice.
At first sight, Hearst patterns may have very sparse occurrences in a corpus.
Nevertheless, words with higher frequencies tend to be covered by Hearst patterns and be IP words.
Therefore, the possibility of encountering OOP pairs is not obvious to assess.

To shed light upon the OOP issue of pattern-based methods, we conduct an analysis on the corpora
and extracted pairs in \citet{P18-2057}.
Considering that nouns tend to be queried more for potential hypernymy than, say, verbs, we only focus on nouns.
In Figure~\ref{fig:distribution}, we show the corpus frequency of all nouns and in-pattern nouns, and draw the following observations.

\textbf{1)} \textbf{$V_\mathcal{P}$ covers well the most frequent nouns in $V$.} For the top-10\textsuperscript{4} frequent nouns, the two lines of dots overlap well, indicating that common nouns are very likely to be involved in Hearst patterns.

\textbf{2)} \textbf{Due to the limited size of $V_\mathcal{P}$, it is unable to cover the tail of $V$.} With the frequency rank below 10\textsuperscript{4}, the two lines begin to separate. Comparing their intersections with the x-axis, it is understandable that a limited number of IP nouns cannot cover both frequent and tail nouns in a vocabulary, whose size is several orders of magnitudes larger.

\begin{figure}[t]
  \centering
  \includegraphics[width=.56\columnwidth]{fig/dist_new.pdf}
  \caption{Corpus frequency of all nouns and IP nouns.}\label{fig:distribution}
\end{figure}

\textbf{3)} \textbf{The likelihood of a noun being OOP is non-negligible.} The two lines enclose a triangular region, corresponding to the likelihood of a randomly drawn noun being OOP. According to our statistics, this region accounts for a non-negligible proportion of 19.9\% of the total area.

With the likelihood of OOP nouns at hand, we are ready to roughly estimate the likelihood of encountering OOP pairs in practice.
Suppose the two words in $q$ are nouns independently sampled from the corpus distribution.
Then the probability of $q$ being OOP, \emph{i.e.,}\xspace infeasible for pattern-based methods, is $1-(1-0.199)^2=35.8\%$.
Even if $y_q$ tends to bias towards more common words, the optimistic estimation is still above 19.9\%.

Table~\ref{tab:detectionDataset} lists the actual portions of OOP pairs in several commonly used datasets \emph{w.r.t.}\xspace $\mathcal{P}$ in \citet{P18-2057}.
Note that neither the datasets nor $\mathcal{P}$ are created in favor of the other.
These actual rates may be above or below the estimated interval of 19.9\%-35.8\%, but are all at considerable levels.
Considering the above analyses, we confirm that OOP pairs are non-negligible in practice and give a positive answer to the first question in Section~\ref{sec:intro}.

\paratitle{Motivation of the Study.}
OOP pairs are problematic for pattern-based methods.
Despite their non-negligible existence, former pattern-based methods~\cite{P18-2057,le2019inferring} boldly classify them as non-hypernymy in prediction.
However, distributional methods are applicable as long as the two queried words have contexts.
Thus, they are ideal to complement pattern-based methods on the non-negligible minority of OOP pairs.

\begin{table}[t]\small
\setlength\tabcolsep{4.4pt}
\centering
\begin{tabular}{lcrr}
\toprule
\textbf{Dataset}&\textbf{OOP (Hyper/All)}&\textbf{Total}&\textbf{OOP Rate}\\
\midrule
\textsc{Bless}&44 / 1,829&14,542&12.58\%\\
\textsc{Eval}&694 / 3,903&13,450&29.02\%\\
\textsc{Leds}&105 / 209&2,770&7.55\%\\
\textsc{Shwartz}&7,209 / 35,266&52,577&67.07\%\\
\textsc{W(Bi)Bless}&0 / 46&1,668&2.76\%\\
\textsc{Hyperlex}&n/a / 107&2,163&4.95\%\\
\bottomrule
\end{tabular}
\caption{Statistics of OOP pairs \emph{w.r.t.}\xspace extracted pairs $\mathcal{P}$. OOP~(All) is the number of OOP pairs while OOP~(Hyper) is the number of OOP with true labels.
\label{tab:detectionDataset}}
\end{table}

\section{Our Approach}\label{sec:approach}

\subsection{Framework}\label{sec:4.1framework}

Our framework is illustrated in Figure~\ref{fig:emnlp_framework}.
It consists of a \emph{pattern-based model} and a \emph{distributional model} cooperating on the \emph{data resource} to answer an arbitrarily queried pair of words $q\in V\times V$.

\paratitle{Data Resource.}
To train a pattern-based model using prior solutions, our data resource includes extracted pairs $\mathcal{P}$ from some text corpus $\mathcal{C}$.
Unlike pattern-based approaches that depend solely on $\mathcal{P}$, our data resource also involves the corpus $\mathcal{C}$ for the sake of the distributional model.

\paratitle{Pattern-Based Model.}
The pattern-based model works on the extracted pairs $\mathcal{P}$ to serve in two roles.
On the one hand, it is responsible for generalizing from statistics on $\mathcal{P}$ to score any in-pattern pair $q\in V_\mathcal{P}\times V_\mathcal{P}$ to reflect the plausibility of a hypernymy relationship.
To this end, it is sufficient to adopt matrix-factorization-based~\cite{P18-2057} or embedding models~\cite{le2019inferring}.
On the other hand, the pattern-based model also provides supervision signals via a \emph{sampler} for training the distributional model.
We will specify this role later.
Formally, we denote the pattern-based model by $f:V_\mathcal{P}\times V_\mathcal{P}\rightarrow \mathbb{R}$.

\paratitle{Distributional Model.}
Different from the pattern-based model defined on IP pairs $V_\mathcal{P}\times V_\mathcal{P}$, the distributional model has a form of
$g:V\times V\rightarrow \mathbb{R}$, \emph{i.e.,}\xspace it should be capable of predicting on any word pair in $V\times V$.
This invalidates the model's dependency on extracted pairs involving $x_q$ or $y_q$.
The separate contexts of $x_q$ and $y_q$ in corpus $\mathcal{C}$ turn out to serve as the basis and input of the distributional model, respectively.
Given the superior performance of pattern-based models on IP pairs~\cite{P18-2057}, the distributional model $g$ is only responsible to answer OOP pairs.

Various choices exist to implement the distributional model.
We may apply unsupervised metrics~\cite{weeds2004characterising,clarke2009context,santus2014chasing} on manual features extracted from contexts of $x_q$ and $y_q$, which are robust to the distribution change of training data~\cite{shwartz2017hypernyms}.
However, the scores of those metrics are not necessarily in the same scale with those output by the pattern-based model $f$ for IP pairs.
Such inconsistency will harm downstream systems which involve the scores for ranking or calculation.

Given sufficient supervision signals from $f$ and the inherent noise of natural language, we implement the distributional model $g$ by a supervised neural-network-based approach.
Specifically, the network encodes the contexts of $x$ and $y$ in $\mathcal{C}$, \emph{i.e.,}\xspace $\mathcal{C}(x)$ and $\mathcal{C}(y)$, to be $\mathbf{x}_h$ and $\mathbf{y}_H$, respectively, and makes predictions by a dot product, \emph{i.e.,}\xspace
\[
g(x,y)=\langle\mathbf{x}_h,\mathbf{y}_H\rangle.
\]
Note that hypernymy is essentially asymmetric, so we distinguish $\mathbf{x}_h$ and $\mathbf{y}_H$ by the subscripts to reflect the asymmetry.
In practice, we adopt networks with separate parameters for $\mathcal{C}(x)$ and $\mathcal{C}(y)$, which is detailed in the next section.

\subsection{Encoding Queried Words}\label{sec:4.2encoder}

To implement the distributional model, we encode $\mathcal{C}(x)$ and $\mathcal{C}(y)$ into hypernymy-specific representations $\mathbf{x}_h$ and $\mathbf{y}_H$, respectively.
There are various off-the-shelf models to encode sentential contexts.
We take the following four approaches.

\paratitle{Transformed Word Vector.}
Instead of working directly on the original contexts $\mathcal{C}(x)$ and $\mathcal{C}(y)$, this approach takes as input the pre-trained word vectors~\cite{mikolov2013distributed,pennington2014glove} $\mathbf{x}$ and $\mathbf{y}$ of $x$ and $y$, and apply two Multi-Layer Perceptrons (MLPs), respectively:
\[
\mathbf{x}_h=\mathbf{MLP}_h(\mathbf{x}),\quad \mathbf{y}_H=\mathbf{MLP}_H(\mathbf{y}).
\]
The intuition is that word vectors roughly depend on the contexts and encode the distributional semantics.
To make the MLPs generalize to $V$ rather than $V_\mathcal{P}$, the word vectors are fixed during training.
Inspired by the \emph{post specialization} in ~\citet{vulic2018post}, it also takes a similar approach to generalize task-specific word vector transformations to unseen words, though their evaluation task is not hypernymy detection.

\paratitle{\textsc{NBoW} with \textsc{Mean}-Pooling.}
Given words $\{c_j\}_{j=1}^n$ in a context $c\in \mathcal{C}(x)$, the Neural Bag-of-Words (\textsc{NBoW} for short) encoder looks up and averages their pre-trained vectors $\mathbf{c}_j$ as $\mathbf{c}$, transforms $\mathbf{c}$ through a MLP, and averages the resulted vectors through a \textsc{Mean}-pooling layer as $\mathbf{x}_h$:
\[
\mathbf{x}_h=\frac{1}{|\mathcal{C}(x)|} \sum_{c\in \mathcal{C}(x)} \mathbf{MLP}_h(\mathbf{c}),\quad
\mathbf{c}=\frac{1}{n} \sum_{j=1}^{n} \mathbf{c}_j.
\]
To obtain $\mathbf{y}_H$, a similar network is applied, though the two MLPs do not share parameters to reflect the asymmetry of hypernymy.
We fix the embeddings of context word vectors during training because satisfactory performance is observed.
Due to its simplicity, \textsc{NBoW} is efficient to train.
However, it ignores the order of context words and may not well reserve semantics.

\paratitle{\textsc{context2vec} with \textsc{Mean}-Pooling.}
To study the impacts of positional information within the context, we also attempt to substitute the \textsc{NBoW} with the \textsc{context2vec} encoder~\cite{melamud2016context2vec}.
In \textsc{context2vec}, two \textsc{LSTM}s are used to encode the left and right contexts $\overrightarrow{c}$ and $\overleftarrow{c}$ of an occurrence of $x$, respectively.
The two output vectors are concatenated as the final context representation $\mathbf{c}$ for the same transformation and averaging as for \textsc{NBoW}.
Formally,
\[
\mathbf{c}=\big[\ \overrightarrow{\mathbf{LSTM}}(\overrightarrow{c});\ \overleftarrow{\mathbf{LSTM}}(\overleftarrow{c})\ \big].
\]
Note that the encoder for $y$ still has separate parameters from those of $x$.

\paratitle{Hierarchical Attention Networks.}
\textsc{NBoW} and \textsc{context2vec} with \textsc{Mean}-Pooling both aggregate every context word's information into $\mathbf{x}_h$ and $\mathbf{y}_H$.
Given several long contexts and the fixed output dimension, it is
vital for encoders to capture the most useful information.
Inspired by \citet{yang2016hierarchical}, we incorporate attention on different words and contexts.
We use a feed-forward network to estimate the importance, and combine the information, of each context word to obtain $\mathbf{c}$:
\begin{align*}
\alpha_j=\text{softmax}\Big(\mathbf{w}_a^\top \tanh(\mathbf{W}_a \mathbf{c}_j)\Big)\text{,}\ \mathbf{c}=\sum_{j=1}^{n} \alpha_j \mathbf{c}_j\text{.}
\end{align*}
Then, another similar network is applied to all $\mathbf{c}^{(i)}\in \mathcal{C}(x)$ to obtain the representation of $\mathbf{x}_h$:
\begin{align*}
\beta_i=\text{softmax}\Big(\mathbf{w}_b^\top \tanh(\mathbf{W}_b \mathbf{c}^{(i)})\Big)\text{,}\ \mathbf{x}_h=\sum_{i=1}^{|\mathcal{C}(x)|} \beta_i \mathbf{c}^{(i)}\text{.}
\end{align*}
For word $y$, the encoder is similar but still has separate parameters from those of $x$.

\subsection{Training the Distributional Model}\label{sec:4.3training}

We train the distributional model $g$'s parameters $\mathbf{\Phi}$ with supervision signals from the pattern-based model $f$.
To make output scores of $f$ and $g$ comparable, we adopt the square error between the two scores as the loss on a pair $(x,y)$, \emph{i.e.,}\xspace
\[
l(x,y;\mathbf{\Phi})= \Big(g(x,y;\mathbf{\Phi})-f(x,y)\Big)^2.
\]

Compared with the potentially large size of the output space, a set of random samples from it suffices to train the parameters $\mathbf{\Phi}$.
For each IP word $x\in V_\mathcal{P}$, we uniformly sample $k$ entries from $\Delta_x$, the column and row involving $x$ in the output space $V_\mathcal{P}\times V_\mathcal{P}$:
\[
\Delta_x=\{(x,y)\mid y\in V_\mathcal{P}\}\ \cup\ \{(y,x)\mid y\in V_\mathcal{P}\}.
\]
The sample for $x$ is done on $P_x$, a uniform distribution over $\Delta_x$.
Finally, our objective is
\[
\min\sum\limits_{x\in V_\mathcal{P}}
\mathcal{L}(x;\mathbf{\Phi}),
\]
where $\mathcal{L}(x;\mathbf{\Phi})$ is the expected loss related to $x$:
\begin{align*}
\mathcal{L}(x;\mathbf{\Phi})=\sum_{i=1}^{k}\mathbb{E}_{(x^{(i)},y^{(i)})\sim P_x} l(x^{(i)},y^{(i)};\mathbf{\Phi}).
\end{align*}

\section{Experimental Setup}

We adopt the widely-used comprehensive evaluation framework\footnote{\url{https://github.com/facebookresearch/hypernymysuite}} provided by \citet{P18-2057,le2019inferring}. To make experimental results comparable, we align the settings as much as possible.

\subsection{Corpora and Evaluation}

\paratitle{Corpora.} We used the 431k \texttt{is-a}\xspace pairs (243k unique) released by~\citet{P18-2057}.
We substitute the Gigaword corpus they used by uKWac~\cite{ferraresi2007building} because the former is not complimentary. This decision does not affect reproducing pattern-based approaches in \citet{P18-2057}.

\paratitle{Evaluation Tasks.} The three sub-tasks include \textbf{1)} ranked hypernym detection: given $(x_q, y_q)$ decide whether $y_q$ is a hypernym of $x_q$. Five datasets \emph{i.e.,}\xspace   \textsc{Bless}~\cite{baroni2011we},
\textsc{Eval}~\cite{santus2015evalution},
\textsc{Leds}~\cite{baroni2012entailment}, \textsc{Shwartz}~\cite{shwartz-goldberg-dagan:2016:P16-1} and \textsc{WBless}~\cite{weeds2014learning} are used. The positive predictions should be ranked higher over negative ones and \emph{Average Precision} (AP) is used for evaluation. \textbf{2)} hypernymy direction classification: determine which word in a pair has a broader meaning. Besides \textsc{Bless}~ and \textsc{WBless}, we also use \textsc{BiBless}~\cite{kiela2015exploiting} and \textit{Accuracy}~(Acc.) is reported for binary classification.
\textbf{3)} graded entailment: predict scalar scores on  \textsc{Hyperlex}~\cite{vulic2017hyperlex}. Spearman's correlation $\rho$ between the labels and predicted scores is reported.

The statistics of datasets are shown in Table~\ref{tab:detectionDataset}.
The three tasks require algorithms to output scores unsupervisedly, which indicate the strength of hypernymy relationships.
Note no external training data is available in the evaluation. Only extracted Hearst pattern pairs may be used for supervision.

\subsection{Compared Methods}

\paratitle{Pattern-Based Approaches.}
We reproduce four pattern-based methods
\emph{i.e.,}\xspace Count, PPMI, SVD-Count, and SVD-PPMI.
As in \citet{P18-2057}, SVD-PPMI is generally the most competitive.

\paratitle{Distributional Approaches.}
We compare with unsupervised distributional baselines in \citet{P18-2057}, \emph{i.e.,}\xspace Cosine, Weeds Precision (WP), invCL, and SLQS.
For supervised distributional baseline, we adopt the strongest model SDSN in~\citet{P18-2101} and take the probability scores of binary classifier as hypernymy predictions. All the 431k extracted pairs serve as true hypernymy pairs and false ones are generated by replacing one of the terms in true pairs with a random term.

\paratitle{Complementary Approaches.}
We adopt SVD-PPMI as the pattern-based model in our framework.
We pre-train 300-dimensional word embeddings with Skip-Gram~\cite{mikolov2013distributed} on our corpus for the use of the distributional model.
Specifically, we compare transformed word vector (\textsc{W2V}), \textsc{NBoW}/\textsc{context2vec} with \textsc{Mean}-Pooling (\textsc{NBoW}/\textsc{C2V}), and Hierarchical Attention Networks (\textsc{HAN})\footnote{Heavy contextualized encoders based on the pretrain-finetune framework did not yield considerable improvement and we focus on efficient traditional encoders which already outperform the baselines. Though we include the BERT encoders in our released code, we suggest to make tradeoffs when choosing encoders as discussed in~\citet{xia2020bert}.}.
The output dimension of our four encoders is set to 300.
The batch size is set to 128 and learning rate to 10\textsuperscript{-3}.
We tuned the sampling size $k$ in $\{1, 3, 5 , 10, 100, 200, 400, 800\}$ on the validation set.
We did not tune other hyper-parameters since the default settings work well.
Our code is available at \url{https://github.com/HKUST-KnowComp/ComHyper}.

\begin{table}[t]\small
\setlength\tabcolsep{1pt}
\centering
\begin{tabular}{lcccccc}
\toprule
     & \multicolumn{4}{c}{{\bf Detection} (AP)}  & \multicolumn{1}{c}{{\bf Dir.}(Acc.)}& {\bf Graded}($\rho$)\\ \cmidrule(r){2-5} \cmidrule(lr){6-6}\cmidrule(l){7-7}
     &\textsc{Bless}&\textsc{Eval}&\textsc{Leds}&\textsc{Shwartz}&\textsc{Bless}&\textsc{Hyperlex}\\ \midrule

Cosine & .106 & .172 & .736 & .175 & .000 & -0.107\\
WP & .100 & .251 & .880 & .283 & .636 &0.147 \\
invCL  & .096 & .211 & \textbf{.887} & .220 & .636 & 0.062      \\
SLQS & .020 & .166 & .423 & .240 & .341 & -0.130 \\
\midrule
\textsc{W2V} & .292 & .255 & .712 & .453 & .767 & 0.313\\
\textsc{NBoW} & .124 & \textbf{.258} & .617 & .500 &  \textbf{.975}  & 0.264\\
\textsc{C2V} & .027 & .258 & .659 & .364 & .791 & \textbf{0.346}\\
\textsc{HAN} & \textbf{.346} & .250 & .602 & \textbf{.574} & \textbf{.975} & 0.309\\

\bottomrule

\end{tabular}
\caption{\label{tab:oov_res} Experimental results on OOP pairs.}
\end{table}

\section{Experimental Results}\label{sec:experiments}

We aim to answer:
\textbf{1)} Are our distributional models supervised well by the pattern-based model?
\textbf{2)} Do they improve our complementary methods over the pattern-based ones?
\textbf{3)} Are complementary methods robust \emph{w.r.t.}\xspace fewer extracted pairs?

\begin{table*}[t]\small
\centering
\setlength\tabcolsep{4pt}
\begin{tabular}{lcccccccccc}
\toprule
    & & \multicolumn{5}{c}{{\bf Detection} (AP)}  & \multicolumn{3}{c}{{\bf Direction} (Acc.)} & {\bf Graded} ($\rho$) \\  \cmidrule(r){3-7} \cmidrule(lr){8-10} \cmidrule(l){11-11}
     &  & {\textsc{Bless}} & \textsc{Eval} &  \textsc{Leds} &  {\textsc{Shwartz}} &  {\textsc{WBless}} & \textsc{Bless} & \textsc{WBless} & \textsc{BiBless} & \textsc{Hyperlex}   \\ \midrule
\multirow{4}{*}{Pattern}
& Count & .486 & .368 & .710 & .288 & .744 & .466 & .690 & .617 &  \textbf{.617} \\
& PPMI & .448 & .341 & .707 & .277 & .734 & .466 & .682 & .611 &  \underline{.603} \\
& SVD-Count & .651 & .434 & .812 & .369 & .904 & .936 & .842 & .801 &  .518 \\
& SVD-PPMI & .764 & .463 & .831 & .409 & .959 & .959 & .871 & .847 & .517  \\
\midrule
Supervised & SDSN & .749 & .458 & .841 &  .432  & .958 & .959 &.874 & .851 & .588 \\
\midrule
\multirow{5}{*}{\begin{minipage}{0.6in}ComHyper (\textit{Ours})\end{minipage}}
& \textsc{W2V} & \ \textbf{.773} &  \underline{.474} & \textbf{.845} & .509 & .957 & .963 & .873 & .849 & .522 \\

& \textsc{NBoW} & .770 & \textbf{.474} & \underline{.844} & \underline{.510} & .958 & \underline{.970} & \underline{.875}  & \textbf{.853} & .523 \\

& \textsc{C2V} & .767 & .472 & .843 & .480 & \underline{.959} & .966 & .872  & .847 & .521 \\
& \textsc{HAN} & \underline{.772} & .473 & .843 & \textbf{.515} & \textbf{.959} & \textbf{.971} & \textbf{.875}  & \underline{.853} & .525 \\ \cmidrule(l){2-11}

& Oracle& .801 & .666 & .876 & .861 & .959 & .992 & n/a & n/a & n/a \\

\bottomrule

\end{tabular}
\caption{\label{tab:expresult} Experimental results on all queried pairs. Best ones are marked bold while second-best ones underlined.}
\end{table*}

\subsection{Performance on OOP Pairs}

To ensure that our supervised distributional models are working effectively on OOP pairs, we evaluate on only OOP pairs under the aforementioned settings.
Because pattern-based approaches trivially give the lowest scores to OOP pairs, we only compare with distributional approaches.

Table~\ref{tab:oov_res} demonstrates the results.
Note that the 46 OOP pairs in \textsc{WBless}\ and \textsc{BiBless}\ are all labeled false, causing undefined AP and perfect Acc.\ scores, so we omit the corresponding columns to save space.
Observing from Table~\ref{tab:oov_res}, except on \textsc{Leds}, our distributional models generally achieve higher scores than unsupervised approaches.
Especially, on the \textsc{Bless}\ dataset, Cosine even gets a zero Accuracy score because it is symmetric and cannot suggest the right direction.
The higher AP and Accuracy scores suggest that, supervised by the pattern-based model, our distributional models can generate better relative rankings within the scope of OOP pairs.

\subsection{Main Results and Case Study}

When facing both IP and OOP pairs, it is not enough to rank both types of pairs separately, since downstream systems usually require comparable scores or a unified ranking.
We evaluate on the entire datasets under the aforementioned settings.
We only compare with pattern-based methods and supervised distributional models because they generally outperform unsupervised ones.

Table~\ref{tab:expresult} provides the main results.
Best results are marked bold, and second-best ones are underlined.
To better interpret the results, we also provide ``Oracle'' scores, \emph{i.e.,}\xspace the upper-bounds that complementary methods can achieve.
For the Detection task, Oracle scores are obtained by assigning OOP pairs having hypernymy relationships (See Table \ref{tab:detectionDataset}) the maximum score and other ones the minimum.
For \textsc{Bless}\ of Direction, the Oracle score is computed by assuming perfect predictions for OOP pairs.
The Oracle scores for \textsc{WBless}/\textsc{BiBless}\ of the Direction task and \textsc{Hyperlex}\ of Graded Entailment are not straightforward to estimate, thus are omitted.

In Table~\ref{tab:expresult}, complementary methods lead to superior results on Detection and Direction tasks.
In eight out of nine columns, the best and second best scores are both achieved by complementary methods.
Especially, large improvements (up to 25.9\%) are observed on \textsc{Shwartz}\ with a higher OOP rate and thus a higher Oracle.
In general, the \textsc{HAN} encoder achieves better performances.
By attending to the most informative contexts and words, the \textsc{HAN} encoder potentially captures distributional semantics that are relevant to hypernymy relationships between queried words.
Note that the relative performances between different context encoders are not necessarily consistent with those in Table~\ref{tab:oov_res}.
This is because the overall performance is not only sensitive to the relative ranking of OOP pairs, but also to their absolute scores.

In addition, with the same extracted $\mathcal{P}$ as supervision signals, our proposed methods show a great superiority over the supervised method~(SDSN in Table~\ref{tab:expresult}).
Both SDSN and our complementary approaches could be regraded as \textit{combining pattern-based and distributional model}.
The key difference is that complementary methods solve Type-I sparsity with a pattern-based model, which proved to be better than distributional ones on this case, while SDSN uses a distributional model (though supervised) uniformly on both cases.

\begin{figure*}[t]
  \centering
  \includegraphics[width=0.97\textwidth]{fig/case_updated.pdf}
  \caption{Case study of two queried pairs from two datasets, with OOP rates and actual ranks.}\label{fig:casestudy}
\end{figure*}

\iffalse
\begin{table*}\scriptsize
  \centering
  \begin{tabular}{c|c|c}
  \toprule
\textsc{Leds}: (\textbf{vicarage},building)&\textsc{Bless}: (\textbf{corkscrew}, implement)&
\textsc{Shwartz}: (\textbf{calamus}, specie)\\
OOP Rate: 7.29\%, Rank: 814/2,770&OOP Rate: 11.48\%, Rank: 3,384/14,542&OOP Rate: 63.91\%, Rank: 1,563/52,577\\
\midrule
  \tabincell{m{0.3\textwidth}}{
  (a)\dots Charlotte Bronte wrote 'Jane Eyre' while staying in the old \textbf{vicarage} in 1844.\dots \\
  (b)\dots In an ecclesiastical coincidence of Trollopian elegance, who should be visiting All Hallows \textbf{vicarage} but the vicar of St Augustine's.
  \dots\\
  (c)\dots A benevolent man, he never forgot his home village, providing money to build a church, a site for the \textbf{vicarage} and setting up a trust fund for annual distribution to the poor and needy \dots
  }
  & \tabincell{m{0.3\textwidth}}{
  (a)\dots This includes scissors, your trusty Swiss Army knife, \textbf{corkscrews} and even tweezers.
  (b)\dots Contained inside the centre of the fountain is a specially designed 2 part stainless steel \textbf{corkscrew} that rotates and pushes the warm chocolate\dots\\
  (c)\dots If you have spent 10, 16 or 30 on one bottle of wine you can afford to buy a decent \textbf{corkscrew} which will open dozens of bottles. \dots}
  & \tabincell{m{0.3\textwidth}}{
  (a)\dots Others are called calamites, since they live among reeds, \textbf{calamus}, and bushes. \dots\\
  (b)\dots Both cannabis and \textbf{calamus} are fragrant, reedlike plants containing psychotropic compounds. \dots \\
  (c)\dots \textbf{Calamus} and products derived from \textbf{calamus} (such as its oil) were banned in 1968 as food additives and medicines by the United States Food and Drug Administration. \dots
  }\\
  \bottomrule
  \end{tabular}
  \caption{Case study of three queried pairs from three datasets, with OOP rates and actual ranks.}\label{tab:case}

\end{table*}
\fi

\begin{figure}
\centering
\subfloat[Detection on \textsc{Eval}]{\label{fig:reduced_eval}
\includegraphics[scale=0.55]{fig/eval_new.pdf}}
\
 \subfloat[Direction on \textsc{Bless}]{\label{fig:reduced_bless}
\includegraphics[scale=0.58]{fig/dir_bless_new.pdf}}
\caption{Performance comparison across different amounts of reducing pairs on \textsc{Eval}\ and \textsc{Bless}.}
\label{fig:reducedPattern}
\vspace{-1em}
\end{figure}

\noindent \textbf{Case Study.} To explain the superiority of the \textsc{HAN} encoder, we exemplify with two true-hypernymy OOP pairs from two Detection datasets, respectively.
Here, the two hyponyms are both uncommon and OOP words.
Therefore, pattern-based models such as SVD-PPMI simply assign the pairs with minimum scores and rank them at the bottom.
But by examining their contexts in the textual corpus, the hypernymy relationships could have been inferred, and they could have been scored higher.

In Figure~\ref{fig:casestudy}, we show the two OOP pairs, as well as their rank according to \textsc{HAN} and the OOP rates of the corresponding datasets.
We also demonstrate the Top-3 contexts scored by \textsc{HAN} and visualize the context- and word-level attention weights.
We observe that \textsc{HAN} can attend to informative contexts and words that help capture the semantics of the OOP word.
For example, in \textsc{Leds}, \textbf{vicarage} is OOP.
\textsc{HAN} suggests three contexts that imply its meaning well.
By reading the context words and phrases highlighted by \textsc{HAN}, \emph{e.g.,}\xspace \textit{commodious residence}, and \textit{collegiate church}, even people not knowing the word may guess it is a type of building.
With our \textsc{HAN}-based distributional model, the pair is successfully promoted to top 50\% in the ranking, well out of and above the bottom 7.55\% of OOP pairs.
Similar observations are drawn for the other pair, \emph{i.e.,}\xspace (\textbf{kinetoscope}, device) with contexts \textit{moving picture viewer}, and \textit{movie projector system}.

We also observe that wrong predictions may be caused by extremely sparse contexts in the corpus such as \emph{famicom} in the dataset \textsc{Shwartz}.

\subsection{Impacts of Reduced Pairs}

To analyze our complementary framework's robustness \emph{w.r.t.}\xspace sparser extracted pairs $\mathcal{P}$, we randomly sample \{95\%,75\%,55\%,35\%,15\%\} of all 243k \texttt{is-a}\xspace pairs, and rerun SVD-PPMI, the best pattern-based approach and our complementary approaches.
In Figure~\ref{fig:reducedPattern}, we only illustrate the results on \textsc{Leds}\ for Detection and \textsc{Bless}\ for Direction.
Observations on the other datasets are similar, thus are omitted.
We have the following observations.
First, with fewer extracted pairs, the OOP rates increase quickly, and all models generally perform worse.
This is not surprising since a sparser $\mathcal{P}$ leads to a less informative SVD-PPMI matrix and less supervision on distributional models.
Second, despite the increased OOP rates, our complementary methods consistently outperform SVD-PPMI
and suffer less from increasing OOP rates especially on \textsc{Bless}.
Finally, among the four context encoders, \textsc{HAN} performs better than the others when the sampled rate is higher than 75\%. However, with lower sampled rates, \textsc{W2V} is more robust than the others on \textsc{Bless}\ but fails to exceed \textsc{HAN} on \textsc{Eval}.

\section{Conclusion and Future Work}\label{sec:conclusion}

We propose complementing pattern-based and distributional methods for hypernymy detection.
As far as we know, this is the first work along this line.
We formally depict two types of sparsity that extracted pairs face, and indicate that pattern-based methods are invalid on the \textbf{Type-\RN{2}}, \emph{i.e.,}\xspace out-of-pattern pairs.
By analyzing common corpora and datasets, we confirm that OOP pairs are non-negligible for the task.
To this end, we devise a complementary framework, where a pattern-based and distributional model handle IP and OOP pairs separately, while collaborating seamlessly to give unified scores.
Oracle performance analysis shows that our framework has high potentials on several datasets.
Supervised by the pattern-based model, the distributional model shows robust capability of scoring OOP pairs and pushing the overall performance towards the oracle bounds.

In the future, we will extend the similar approach to multilingual~\cite{yu-etal-2020-hypernymy} or cross-lingual~\cite{upadhyay-etal-2018-robust} lexical entailment tasks.
Moreover, one interesting direction is to use hyperbolic embeddings~\cite{le2019inferring,balazevic2019multi} for pattern-based models due to their inherent modeling ability of hierarchies.

\section*{Acknowledgements}

This paper was partially supported by the Early Career Scheme (ECS, No. 26206717), the General Research Fund (GRF, No. 16211520), and the Research Impact Fund (RIF, No. R6020-19) from the Research Grants Council (RGC) of Hong Kong.

redpajama_set_name: RedPajamaArXiv


The Gorbals Community Choir remind us that weeping may endure for the night, but joy comes in the morning!
Remember there are NO Givin it Laldie groups this Wednesday as the team take some time to prepare for the term ahead! Enjoy one more week of rest and relaxation !


The two dropped gloves to brawl before Ovechkin pummeled rookie Svechnikov with three right jabs. Svechnikov, who went to hospital after the fight, will miss the next game (and probably more) with a concussion.
To nobody's surprise, it was announced later that he would not return for the remainder of the game.
Washington leads the series, 2-1. Ovechkin seemed to be declining at first but then dropped gloves.
"If you watch the video, he slashes him twice - Ovi, whack, whack - then Svech gets him back", Brind'Amour said, per ESPN's Emily Kaplan. I'm still sick to my stomach about it.
"So, it's a little but frustrating, because he got hurt". So, if that's the case, it's a little different. And that's Ovi, not our guy. "He's played 90 games, he never fought in his life, I'm pretty sure Ovie knew that. I'll just leave it at that". So that stuff bothers me.
Last night's (Mon., April 15, 2019) Stanley Cup Playoffs series between the Washington Capitals and Carolina Hurricanes got very interesting. Ovechkin won in an absolute landslide.


Have you ever heard the phrase "you can't out train a bad diet?" What this phrase typically means is that even if you have an intense and regular work out regimen, you won't achieve optimal results if your dietary intake is poor. While what exactly constitutes a "bad diet" varies from one person to the next, if your exercise program isn't giving you the results you want, it's time to take a hard, honest look at your daily dietary intake.
Myfitnesspal is a free online and mobile nutrition and exercise tracker with an extensive food database, customizable diet tracking, exercise database and user community.
Convenient integration with other popular systems like Fitbit (http://fitbit.com), BodyMedia Fit (http://www.bodymedia.com) and Runkeeper (http://runkeeper.com); Free mobile app available for iOS, Android, Windows AND Blackberry; Extensive user database contains nearly every food you can imagine and makes it easy to quickly log and/or add new foods; ability to customize your dietary goals as well as which macros you see; Built-in and well-designed exercise tracker allows you to track everything with one tool.
Macro customization is limited to five items; you have to create an account to use the tool.
SuperTracker is a free online tool developed and maintained by the Center for Nutrition Policy and Promotion (CNPP), an organization of the U.S. Department of Agriculture (USDA). SuperTracker data and guidance is based on the MyPlate (http://choosemyplate.gov) food guide developed by the USDA and therefore, focused on a U.S. diet.
Although it's recommended for best results, you don't have to create an account to use the tracker; Not focused solely (or primarily) on weight loss – you can choose other goals to track like (1) amount of physical activity, (2) target food groups, and (3) monitoring specific nutrient intake. Amazingly detailed reports to show macro- and micro-nutrients and empty calories (calories that do not provide any nutritional value).
Limited food database and only very useful if you only (or mostly) eat regular whole foods and simple recipes with few ingredients; limited ability to manually quick-add foods to your log; no mobile app; government created and maintained, so in times of governmental crisis – like last year's shutdown – the site may not be available (I learned this the hard way).
FitDay is a multi-tiered application that seems highly geared toward weight loss. A very basic online version of the nutrition tracker is available for free. Additional features are provided in a paid premium version. You may also pay to download and install a PC version to use offline. A mobile app is also available.
Detailed reports show macro- and micro-nutrients, although not as many as Supertracker; easy-to-use interface; opportunity to connect with a live dietitian for dietary advice.
Online basic online account is free – you must pay for a PC version ($29.99) or mobile app ($1.99 for iOS); No ability to select which macros you want to track; Doesn't track sodium intake (this is a big deal for potential users who must watch their sodium intake); Can't customize your dietary goals (in the free version); limited food database.
Easy to use, Detailed reports directly integrated onto the main food diary screen (although not customizable); built-in exercise tracker.
Calio is another option. They are a company I did some videos for as a spokesperson in 2014 and are in beta. They try to make it easy for people on the go to track their calories using voice recognition to enter in foods instead of having to type everything in. Pretty neat!
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One of the most prominent and impressive landmarks in the state of Washington if not the entire Pacific Northwest, Mount Rainier towers majestically over the Cascade Range. Located just a little more than 42 miles from Tacoma, Mount Rainier is recognizable to everyone who lives in the region, however there are a number of interesting facts about this peak that many Washingtonians may not be aware of. Here at FIAT of Tacoma we've collected some of the most intriguing facts to help you better understand this iconic mountain.
Where is Mount Rainier? – In west-central Washington state, in the Mount Rainier National Park, 59 miles south-southeast of Seattle.
How High is Mount Rainier? – It is the highest mountain in Washington, with a summit elevation of 14,411 feet.
Is it an Active Volcano? – Yes, it is an active volcano, and is considered one of the world's most dangerous due to the extensive glacial ice at the peak.
When Did it Last Erupt? – The last time Mount Rainier erupted was between November and December of 1894.
How Did it Get its Name? – George Vancouver named the mountain after his friend Rear Admiral Peter Rainier in 1792, and the name was made official by the U.S. Board on Geographic Names in 1890. Local Native American tribes such as the Nisqually, Puyallup, Squaxin Island, Muckleshoot, Yakama, and Cowlitz referred to the mountain as Takoma, Tahoma, Tacobeh, or Pooskaus.
Who was the First to Climb Mount Rainier? – Hazard Stevens and P.B. Van Trump were the first to ascend the mountain. They accomplished this feat in 1870.
When was the Mount Rainier National Park Established? – Mount Rainier became the fifth national park in the U.S. in 1899.
When is the Park Open? – Mount Rainier National Park is open year-round. To confirm road conditions and operating hours of the various visitors centers call 1-360-569-2211.
How Much Does it Cost to Enter the Park? – The single vehicle fee is $30 and it is good for 7 days. A campground fee is $20 per night. The park also offers an annual pass of $55, a $15 per person walk in fee, and a nightly campground fee of $60 for groups of 25 to 40 people.
Can You Hike on Mount Rainier? – There are a number of hiking trails throughout Mount Rainier National Park, including a portion of the Pacific Crest Trail that enters along the park's eastern boundary. If you plan to camp overnight you are required to get a permit. Mount Rainier itself is not suitable for hiking, as the steep and rugged terrain is reserved for climbers only.
Can I Climb Mount Rainier? – Thousands of people climb Mount Rainier every year, but it is important to be physically prepared for the ascent. Check the park climbing guidelines before planning your trip.
From the Museum of Glass, to Puget Sound, and the Mount Rainier National Park, there's a lot to do and to see in the Tacoma area. Here at FIAT of Tacoma we're always on the lookout for fun and interesting attractions to recommend to our Olympia and Lakewood customers. If you know of places or events that we need to check out please feel free to contact us.


Today is the March equinox, a day when the Sun is above the horizon for exactly half the time everywhere on Earth. According to the astronomical definitions of the seasons, this day marks the beginning of spring in the northern hemisphere and of autumn in the southern hemisphere.
On the day of the equinox, the Sun will appear to rise from the point on the horizon which lies due east, and set beneath the point which lies due west. This happens as the Sun's annual journey across the sky, through the constellations of the zodiac, carries it across the celestial equator. As a result the Sun appears directly overhead at noon on the Earth's equator.
On the day of the equinox, the Earth's poles are not tilted towards or away from the Sun, meaning that the Sun is above the horizon for half the time at all latitudes. Image courtesy of Przemyslaw Idzkiewicz.
Equinoxes occur twice a year – in March and September – once when the Sun is travelling northwards, and once when it is travelling southwards. The position of the Sun at the moment of the March equinox is used to define the zero point of both right ascension and declination.
In practice this is not exactly the case, however, because of a phenomenon called the precession of the equinoxes. Over hundreds of years, the direction of the Earth's spin axis in space changes because it acts like a gyroscope. This means that the location of the equinoxes creep across the sky at a rate of around 50 arcseconds each year.
Astronomers quote right ascensions and declinations based on the configuration of the Earth's path around the Sun on January 1, 2000. Even in the years that have passed since the year 2000, the precession of the equinoxes has moved them by several arcminutes.
The table below lists the exact position of the Sun at the moment it passes over the equator in March 2017.
Sun 23h59m -00°05' Pisces 32'07"


ORWELL — In 1990, Orwell resident John Tester signed up his 6-year-old son for tee-ball in his then hometown of Union City, Calif., just outside of Oakland. Tester learned the team needed a coach, and he took the job.
The only catch was that Union City Little League baseball required its coaches to umpire.
That April Tester for the first time took the field ready to call them as he saw them.
VERGENNES — The loudest phase of Green Mountain Power Corp's major project to rebuild parts of its Vergennes dam was set to start Monday morning, when demolition experts will begin what is expected to be about two weeks of a half-dozen blasts a day to clear out bedrock to allow replacement of what company officials call decaying piping.
Traffic on West Main Street will be stopped when blasting occurs, and motorists can expect delays.
MIDDLEBURY — Irena Sendler has become a national hero in Poland, and deservedly so. During World War II, she risked her life to rescue 2,500 Jewish children from the Warsaw ghetto, often knocking on the doors of Jewish homes and convincing parents to entrust her with their children.
VERGENNES — Last week the old Vergennes Laundromat on Main Street was mostly empty, except for a few stacks of boards and the hum of power tools emerging from the open doors.
Inside, 25-year-old Julianne Jones and fiancé Didier Murat were working atop the newly poured concrete floors, cutting and measuring the wood that would become the bathroom walls. Along the wall in the middle of the long room was a platform, sitting at the ready for the brick oven that will form the centerpiece of Jones's bakery when it opens in September.


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Please help to figure out the issue.


Today Year 1 walked to St Benedict's Church to learn more about the topic Christianity. All the children walked sensibly to the Church full of enthusiasm and excitement to see what the Church looked like and what we were going to learn inside.
Reverend Anne welcomed everyone into the church with open arms. She began by telling us about the faith Christianity and we discussed why people might visit a Church. We then performed a pretend wedding and christening and dressed the children up to act out the important roles for each of these days! It was great fun!!
After that, we walked around the Church to look at the different objects and to ask questions to Reverend Anne about the things we found. We looked at a font, prayer tree, prayer corner, St Benedict, Jesus on the cross, the altar and many more things. Lots of children loved how the Church was decorated with doves as a sign of peace.
Once the children had asked Reverend Anne many questions, we began to sketch different parts of the church in our books. The children looked very closely at the finer details of some of the objects and really tried hard to replicate this in their work.
We left the church singing Nativity songs and played eye spy for most of the way back to school!!
What an amazing day. Reverend Anne praised all of the children on their behaviour and said they were a pleasure to have in her church! Well done Year 1, I am very proud of you all today for taking in all the new information and having a go at all of the different activities in the day!!
What was your favourite part of the visit?


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Home » News » Press Releases » Gerry Gebel and David Brossard Headline Three Sessions at Identiverse 2019
Gerry Gebel and David Brossard Headline Three Sessions at Identiverse 2019
Kelly O'Dwyer-Manuel · Tuesday, June 11th, 2019
Axiomatics Experts to Cover Critical Identity and Security Topics
CHICAGO – June 11, 2019 – Axiomatics, the leader in externalized, fine-grained dynamic authorization, today announced that Gerry Gebel, vice president of business development and David Brossard, vice president of customer relations, will present at Identiverse 2019, June 25-27, 2019 at the Washington Hilton in Washington, D.C.
Identiverse brings together the brightest minds in the identity and security universe to share the tools, technologies and best practices emerging today. The show teaches businesses how to unlock digital experiences that are secure and seamless for everyone and reach the full potential of the digital economy.
On Tuesday, June 25, from 2:40-3:40 p.m. EST, Gebel and Brossard will speak together during a session titled "Policy-enabling your services: elastic, dynamic access control for your APIs, microservices and data." The session will cover how businesses can address security concerns through policy-driven authorization in a way that meets the needs and expectations of application developers, owners and auditors.
"APIs are fundamental to build and run countless applications and services because developers consume data and services more readily through APIs," said Gebel. "I'm excited to join my colleague Tuesday to share with attendees how they can tackle API security by combining OAuth and OpenID Connect with dynamic authorization. I'll be back on the stage Wednesday to discuss my still evolving thoughts about social login and single sign-on, and how they present challenges for personal privacy."
On Wednesday, June 26 from 11:35-noon EST, Gebel will present an individual session called "Bring Your Own Identity: A Skeptical look at Social Login and Single Sign-On." On Thursday, June 27, from 4:35-4:50 p.m. EST, Brossard will examine how fine-grained dynamic authorization solves complex access control cases during a session named, "The Developer's Guide to Policy-Driven Auth'z (or: how to make the right decision 100 percent of the time)."
"There are several benefits to policy-driven authorization businesses can take advantage of in the digital age, including simplifying complex scenarios like GDPR compliance and export control," said Brossard. "Technologies like dynamic authorization delivered with Attribute Based Access Control (ABAC) empowers developers to implement policies without having to write a single line of code, enabling them to solve complicated authorization decisions easily."
In its 10th consecutive year, Identiverse has become one of the largest identity security conferences in the United States. The show includes over 2,000 delegates, 200 sessions and 65 vendors all within four days.
For more information about Axiomatics, visit axiomatics.com. To attend Identiverse 2019, visit https://identiverse.com or follow the #IDENTIVERSE hashtag on Twitter.

redpajama_set_name: RedPajamaCommonCrawl


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A new $3 million grant from the Department of Defense will help the University of Central Florida's RESTORES clinic develop its own virtual reality software to treat first responders, veterans, active duty personnel and civilians with post-traumatic stress disorder and other trauma-related disorders.
The clinic currently uses third-party software. However, the new grant will allow RESTORES to develop its own software to address a wider range of scenarios, such as those faced by first responders and survivors of mass shootings or sexual assault as well as situations veterans and active duty personnel face. The grant will also help test ways to increase the software's effectiveness.
"We are honored that the Department of Defense has recognized the success of our treatment program and continues to entrust us with funding that allows us to provide cutting-edge treatment based on rigorous clinical science," says Deborah Beidel, director of the RESTORES clinic.
RESTORES began in 2010 with the purpose of treating veterans and active-duty military personnel with combat-related PTSD. After the Pulse night club shooting, services expanded to include first responders and survivors of mass shootings. Since opening, it has treated more than 450 combat veterans and first responders.
In September 2018, the PTSD clinic at UCF RESTORES became known as the Rosengren Trauma Clinic, after a generous donation from Julia and Jim Rosengren '81, whose son benefited from the clinic's treatments.
Results have shown that 66 percent of combat veterans and 73 percent of first responders no longer met the clinical definition of PTSD after treatment.
RESTORES' PTSD treatment includes a three-week program that combines virtual reality and group therapy sessions on guilt, anger, resocialization, insomnia and more. The virtual reality sessions expose patients to videos of scenes of traumatic situations, such as war, and couple these images with the sounds of gunfire and explosions along with the smells of smoke and gunfire.
The idea is that exposing patients to the traumatic experiences will help reduce their stress about them.
RESTORES is located in the psychology building on UCF's main campus in Orlando, and it also has a recently opened Brevard PTSD Clinic located in the UCF Cocoa building on the Eastern Florida State College campus. The treatment program currently provides treatment at no cost to Florida residents. The program has been awarded more than $10 million worth of grants in the past few years.
Beidel received her Ph.D. and M.S. in psychology and M.Ed. in rehabilitation counseling from the University of Pittsburgh and her B.A. in psychology from Pennsylvania State University. She has been with UCF since 2007.


Your design is sound, your contractors are experts and your equipment is top-notch. Why would your building be at risk for a plumbing leak?
Plumbing leaks contribute heavily to nearly $10 billion in homeowner insurance claims due to water damage each year, reports the Insurance Information Institute. And Zurich Insurance notes that water damage is the leading cause of property damage in offices, apartments and other real estate facilities.
Leaks are inevitable. They are not a reflection of design, quality or workmanship. Plumbing fixtures, joints and water-fed appliances are simply prone to failure over time. And when minor leaks go undetected for hours, days or even weeks, the resulting property damage can turn catastrophic — especially in common-risk commercial, residential or office buildings.
As water damage emerges as a leading source of claims, insurance companies are increasingly offering discounts for properties that employ plumbing leak detection, even mandating the installation of such systems if a claim has been paid out in the past or a property employs high-end finishes. Plumbing engineers and architects are driving adoption of leak detection as value-added engineering on their projects, and forward-thinking mechanical contractors and property owners are also helping to make plumbing leak detection a more mainstream technology.
A burst washing machine hose can release 650 gallons of water in a single hour. A plenum water heater can leak for days and no one will know until the ceiling collapses. Dishwasher leaks in residences and breakrooms can go undetected for weeks, causing mold issues in addition to water damage. In a commercial building left unattended over the weekend, a burst pipe can lead to ankle-deep water come Monday morning. Undetected slow leaks can lead to skyrocketing utility bills. Water can quickly spread and displace multiple tenants, damaging a building owner's reputation and requiring them to deal with expensive insurance claims or even loss of coverage.
These are real problems your clients face every day; you are in a unique position to recommend proactive measures that can protect them.
The good news is that specifying leak detection technology does not have to be a frustrating or complex process. Solutions are available for even the most challenging spaces, from covering large facilities to monitoring within plenums.
Leak detection is a simple concept, but it is by no means a one-size-fits-all deployment. The first step in choosing a solution for any given project is determining the potential leak risks in play.
What water-fed appliances and general plumbing layout do you need to monitor? Is protection needed for mission-critical equipment, data or priceless possessions? Are there tenant-related concerns that could lead to water-related problems, such as in a memory care or assisted-living facility? Are you dealing with a medical facility with many sinks or equipment using water filtration, such as dialysis machines? Is reducing water waste or consumption a critical concern to the building owner?
Once you've identified what you need to monitor, determining the desired outcome when a leak is detected is critical in specifying the best technology for the job. You should consider things such as: Who is using the water sources in question? What are the ramifications of shutting off the water supply? What is the potential impact of water damage mitigation activity on tenants, residents and building owners?
This information will help you determine what type of response you require from a leak detection system. Is a simple audible alarm sufficient to alert people of a leak? Or do you also need to automatically shut off the water supply to an individual appliance, entire office/apartment, floor, building section or even to the entire building? Is automatic notification of a leak required via text message, email or smartphone/tablet app? Do you need to tie in notifications to a building management or security system?
For retrofit/refurbishment projects, is there access to the piping in the desired shut-off locations? Does the contractor have enough physical space to install the shut-off device in the desired location?
Is electrical power required and available in the location?
Do you need to account for plumbing or appliances located in plenum spaces?
What pipe and valve sizes are you working with? You will find some mitigation systems offer limited valve size options.
Valves must be rated for potable water (drinking water), adhering to both NSF/ANSI 61 and NSF/ANSI 372 to meet the federal Safe Drinking Water Act.
Are battery-operated components acceptable to the client/tenant? Does easy access to such components exist to change the batteries?
What happens to the system when it loses power? Will the valve automatically close and is it a positive or negative outcome for the client/tenant?
How will the system be reset if the installation of the valve is behind the wall?
Your final major consideration is the technology driving the system you specify, and you will usually encounter two options: point-of-contact or flow.
A point-of-contact system typically uses a conductivity sensor(s) to detect a leak. Sensors are deployed in areas where water is likely to first accumulate in the event of a leak — inside a pan or on the floor/surface. Water must come in contact with the sensor to activate the system, which may or may not include a valve to turn off the water feed automatically.
Point-of-contact technology can support a wide range of valve sizes and incorporate many different sensor types and accessories to accommodate a wide range of applications. Since sensors are deployed in specific locations, and wireless sensors are often addressable, it is typically easy to identify leak origination points. These systems do require that you do a good job predicting where water may accumulate, and more steps are involved in system installation than with flow monitoring solutions.
Depending on the facility, loss of unsecured sensors also can be a concern.
A flow monitoring system is installed directly on the water line. It constantly monitors usage, using preset triggers for volume (low, medium or high usage) or time (for example, 30 minutes of continuous flow before water is shut off), and it may or may not include a valve to turn off the water feed automatically. With no external sensors, flow technology is very discrete, and it can offer additional valuable data on overall water usage.
Flow systems are typically only available for 3/4-inch and 1-inch pipes. Installation can require significant feedline access, complete system drain-down and purge, and the like. The biggest challenge of using flow monitoring for leak detection is that it offers little indication of a leak's origination point. False alarms also can occur from simple changes in behavior or usage patterns.
While plumbing leaks are a simple fact of life, the damage caused by them is almost completely preventable.
Building owners, tenants and industry forces are realizing the benefits of proactive leak detection and water damage mitigation systems — preventing business and homeowner disruption, property damage, additional claims from other needed remediation such as mold, loss of data and human life, and water conservation.
Leak detection specification is straightforward as long as you understand the desired outcomes, system design considerations and decision points.


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35 Lara Robinson's hot Photos Including Cute And Beautiful Ones
11 months ago 4 months ago
The Australian singer, actress, and student, Lara Robinson, was born in Australia on the 1st February 1998, and ages 20 years by now. The beautiful Melbourne actress is popular for her amazing work in "Knowing" in 2009, Cloudstreet in 2011 and Upper Middle Bogan in 2013. Most of them are popular TV series and drama that earned Lara recognition. Upper Middle Bogan shows two families residing at opposite ends while Cloudstreet again shows working-class families of Australia living together in the One Cloud Street. Lara has appeared in theatre productions, in the TV series, and in several movies. Nevertheless, the actress is sexy and you may check out the hottest of pics on our website.
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Lara Robinson made the appearance in the remake of "Long Weekend", an Australian thriller movie starred by Claudia Karvan and also in the science fiction film "Knowing" starring Rose Byrne and Nicholas Cage. Lara also made guest appearances in popular TV shows and series including The Elephant Princess, City Homicide and then she joined Winners & Losers casting crew in 2012 as a student named Tilly Young. Robinson is also a popular dancer and musician. Her popular films are "Work in Progress", "Long Weekend", "Knowing", "Saved", "The BBQ", "Catch Perfect" and many more. We have shared 35 Lara Robinson's hot photos including cute and beautiful ones.
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FundingGenie started the Help Hurricane Sandy Victims Crowdfunding campaign to encourage everyone to help each other and for the community to recover fast.
The massive storm hit the north eastern coast of the United States on October 24, 2012, leaving at least 285 people dead. The cost of the damage is estimated at $68 billion dollars as data released this June recorded.
The shops participating will be providing discounts and special offers. This is to encourage everyone to help and donate for the recovery of businesses in New Jersey (NJ) and New York (NY).
Donors are entitled of these discounts and special rewards. By participating, donating, and buying, the whole city will end up being picked up from the disaster.
Every cent counts. It doesn't matter how much you give but what matters is how many people will be helping.
Lilly Services plus business affected by the hurricane and the Business Supporting and Relief and Recovery Efforts joined forces to organize this event.
This aims to help businesses to rebuild. Everyone's support, especially those who were distressed by Hurricane Sandy, is much needed.
This organization is helping communities affected by natural disasters and victims of injustice and violence like the shootings at Newtown, Connecticut. They encourage people to give a hand with the campaigns they launch.
A helping community is sure to recover fast. With the number of people showing love and support, nothing is even close to impossible. Giving can save thousands of lives and more in the years to come.