Suffering injuries due to the negligence of someone else does not typically require a full jury trial to obtain the compensation you deserve. At the Law Offices of Robert Olkowitz, P.C., we are dedicated to helping our clients achieve full and fair compensation for all expenses related to their injuries efficiently and without undue stress.
When the responsible parties for an accident are easily identifiable—and full recovery from your injuries is likely within a reasonable period of time—your Red Bank personal injury lawyer at the Law Offices of Robert Olkowitz, P.C. helps you pursue an insurance claim. Making a claim against the negligent party's policy is inexpensive, and it may generate compensation quickly. While this is typically a do-it-yourself project, we provide valuable advice during your free initial consultation to ensure you handle interviews with claims adjustors properly.
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If your injuries require long-term recovery or leave you permanently disabled, we may recommend taking your case to court. Jury verdicts are often the best option for winning the full award you deserve to ensure you are compensated for a lifetime of expenses.
If you or your loved ones suffered an injury, contact the Law Offices of Robert Olkowitz, P.C. to set up a free consultation today. We only receive a fee if we obtain a recovery for you!


Alnico 3 P.A.F. style humbuckers are getting a bit of buzz (ha! a pun!) in the pickup world. A little more so this past year. There is one pickup mod guru that preaches the benefits of Alnico 3, and has been for some time. It seems that Alnico 3 is most aligned with jazz or blues styles. But I also know that a lot of the rock and hard rock songs that many of us use as a reference are powered by "vintage output" humbuckers.
You know that I am very impressed by the custom spin that Mojotone put on a Hot Classic set for me. Mojotone already uses Alnico 4 in their "59 Clone" Humbucker, so they are the perfect ones to go-to for when you want that big 70s area rock vibe from a Hot Classic set made custom with Alnico 4. So, with that in mind, they were the ones for an Alnico 3 vintage-inspired humbucker. Maybe something that'd go great in a Les Paul with a "dirty lemon" burst finish.
As always, Mojotone can get an order out quickly. At about the same time, I grabbed a Peavey 6505 cab (loaded with Sheffields). I also have a Fractal Axe-Fx on loan, pending a possible purchase. Consequently, the Alnico 3 humbucker set is running through all sorts of variables for a very thorough evaluation.
The vintage-level DC resistance shouldn't give anyone a pause. And yes, Mojotone did have to talk me off the ledge at suggesting higher levels. LOL! Nonetheless, you guys know me, and all pickups get hit with high-energy hard-rock vibes for testing.
In the bridge position, there is a reserve of airy chime and an extra dimension to the articulation. I am really digging the bloom and the responsiveness to finesse. Depending on your style, you can get polite jazz or rude blues. Then you can take it up a notch for hard rock, without the restrictions of a lot of the compressed voicings out there. You get a bit of a sweet sizzle for lead work. The high end cuts through without being as sharp as an Alnico 5 and the mids can deliver a growling snarl that keeps things settled into the mix.
Looking at the neck, you get a balanced humbucker that's voiced to give the set a lot of versatility. A little bit of bite in the highs, some snap in the mids and a touch of punch in the lows. Together, it lends itself well to some classically vintage middle position characters.
But you know, it doesn't have to be a P.A.F.-ish Alnico 3 humbucker set. Maybe you want a different coil offset? Or different resistance? Or long/short legs? Or perhaps you see something "Coming Soon!" that never comes! The guys at Mojotone are always excited and positive when it comes to a new idea.


text:
Synopsis: Injured while risking his life to save an angry German shepard, Chicago Firefighter Jack Moniker retires and moves to a small carribean island named St. Nicholas. There, he is befriended by the owner of a run-down resort, Ernest Reed. Greedy developers are schemeing to wrest Reed's coveted beach front property from him for non-payment of taxes. Jack comes to Ernest's rescue, and together they renovate and reopen the resort catering to affluent Americans. The film follows the zany exploits of the proprietors, guests and various colorful island denizans, as they break in the new "Club Paradise".
Director(s): Harold Ramis
Actors: Robin Williams, Peter O'Toole, Rick Moranis, Jimmy Cliff
How come the dog gets to sit in there
while we're freezing our ass off out here?
It would be cruel to let him out
on a night like this.
- He's just a dumb animal.
- No, Dave, we're the dumb animals.
Keep it right there.
Over to the side. Yeah, yeah. Stay back.
A little girl?
- I'm coming. All right, I'm here.
- Yeah.
Where were you on the stairway, asshole?
- I was looking for you.
- Oh, yeah, looking for me.
This is my kind of night, Dave.
The wind chill factor's 10 below zero...
that building's an inferno,
my stomach's a goddamn volcano...
and I got an icicle of snot
hanging from my nose.
That's it. This is my last fire.
I'm hanging up my hose.
Bullshit, Jack.
I've heard that from you 1,000 times.
- Bullshit? Oh, yeah?
- Bullshit.
Read this, pal, and weep.
"Paradise for Pennies. A Garden of Eden...
"built on a secluded lagoon, with its own
private dock and speedboat included.
- "Contact Mr. Banks, Saint Nicholas."
- Where the hell is Saint Nicholas?
- It's in the islands, Dave.
- Which islands?
Nice, warm islands.
Go on, ma'am.
- My baby is in there.
- Yes, I know.
Damn it. Hand me that crowbar.
Let me know what's happening.
Let me know what the spread is.
- Move along!
- Where's he going?
Get in after him!
Oh, shit!
Come on, baby. It's me, Jack.
Come on.
Come on, baby.
You want to play?
You furry piece of shit.
Get the net! Come on, babe.
Come on, baby. Yeah! Get it, baby.
Jack, Jack! Oh, Jesus! Jack!
- Jack.
- Dave.
I'm here, Jack.
Son of a bitch.
Jack, can you hear me?
Jack!
Limbo time, everybody.
Do you mind, Jack?
lt's awfully loud, isn't it?
"Awfully loud, isn't it?" Quite true. Sorry.
Terribly sorry.
Jack, this is Captain Toby Prooth
and Phillipa Lloyd.
They just came in from Antigua.
Toby has the most beautiful sloop.
Yes, he does. Are you cast or crew?
Strictly crew.
And, of course, you know the Governor.
Oh, the formerly honorable
Anthony Croyden Hayes. Your Excellency.
Good gracious, man, are you still here?
You remind me of a sort of strange
vegetation that just popped up...
and now we can't get rid of.
It seems that you've been here for a year.
Oh, it's just been five months. But tempus
really fugits when you're having fun.
That's what I love about you tourists:
the instant you get your pink feet...
into some sand, you start thinking about
giving up the dry-cleaning business...
in Cleveland and moving to the islands.
Are you in the dry-cleaning business,
Mr. Moniker?
Hat-blocking? Xerox repair?
Fireman, sir. Chicago fireman. Retired.
- That's interesting.
- There's a smashing yacht club...
in Chicago. Belmont Harbor.
Well, you see,
yachting's a little tough in Chicago...
'cause they tend to shoot at you
from the South Shore.
Yes, you have a nice, steady kind
of violence in the States. Very predictable.
Here in the jungle, it's basically long,
tedious periods of sweltering doldrums...
punctuated by the occasional bloodbath.
I think we're due for one at any minute.
Now, now, Tony...
don't start those wild rumors
about revolution again.
Well, it happened in Kenya, didn't it?
They dragged those fat burghers
from their beds in the dead of night...
forced them to drink blood
through a severed sheep's penis...
and then hacked them to pieces
with machetes.
It isn't bad as all that.
Besides, I don't think there are any sheep
on Saint Nicholas.
I heard about this guy in Cicero.
He stiffed a loan shark. So a couple
of these guys got him. Cut off his arm...
beat him to death with the arm...
Harold Allen Ramis (November 21, 1944 – February 24, 2014) was an American actor, director, writer, and comedian. His best-known film acting roles were as Egon Spengler in Ghostbusters (1984) and Ghostbusters II (1989) and Russell Ziskey in Stripes (1981); he also co-wrote those films. As a writer-director, his films include the comedies Caddyshack (1980), National Lampoon's Vacation (1983), Groundhog Day (1993), and Analyze This (1999). Ramis was the original head writer of the television series SCTV, on which he also performed, and he was one of three screenwriters of the film National Lampoon's Animal House (1978). more…
All Harold Ramis scripts | Harold Ramis Scripts
Discuss this Club Paradise script with the community:
"Club Paradise" Scripts.com. STANDS4 LLC, 2020. Web. 20 Jan. 2020. <https://www.scripts.com/script/club_paradise_5697>.

redpajama_set_name: RedPajamaCommonCrawl


What exactly is a Probate Sale. When a person passes away, his/her husband/wife or person listed in the will can sell a property. What happens if there is no husband or wife or will? Believe it or not, we get calls from relatives every week that can't sell a property of the deceased relative because there was no will! If you don't have a will-hire an attorney and get one ASAP before it's too late! If there is no will the property must go through a probate process, where a court appoints a personal representative who will be in charge of the estate. Probate might take from 2 months to a year or even longer. Imagine what happens during this period? Property might get vandalized, behind on mortgage payments and could be sold at the foreclosure auction. If probate process is not over, you might not have enough time to buy the house back from the sheriff because redemption period would be over. Ups, you just lost a big chunk of cash. What we can do to help you? If there is some equity in the property we will pay you $5000 and handle everything. If there is a lot of equity and you are able to come to court with our attorney and state your case, we can pay you more. How much more? Contact us, we will review your case and you will find out within 24 hours.


An interactive design methodology of HIDiC with finite internal heat exchange stages is proposed. When the number of heat integrated stages is limited, the number of possible combinations of heat integration increases. Thus, the optimal design problem usually becomes a complex mixed integer nonlinear programming problem. In this research, a novel design methodology using modified Ponchon-Savarit diagram is proposed. The first modification of the diagram is the introduction of the reversible distillation curve, which shows the enthalpy profile at reversible condition. By using this curve, the target enthalpy profile in the column can be determined and the plausible stages for heat exchange can be determined by taking the predetermined theoretical stages into account. The second modification is the integration of the T-xy diagram with H-xy diagram. By doing so, the temperature difference of side exchangers can easily be identified, and it becomes possible to determine the pairing of heat integration stages between the rectifying section and the stripping section in consideration of the heat transfer area of side exchangers. The conditions obtained by the diagram methodology can be directly used in rigorous process simulation. Case studies demonstrated that the results obtained from the proposed methodology have good agreement with the conditions obtained through numerical optimization. The designer can modify the design condition interactively by checking the obtained diagram. Thus, the proposed methodology is especially beneficial at the preliminary design stage of HIDiC. (C) 2013 Elsevier Ltd. All rights reserved.


text:
Researcher evaluates Alexander Technique in pain clinics
by University of the West of England
(PhysOrg.com) -- A researcher from the University of the West of England is looking into the use of the Alexander Technique (AT) as a teaching method to help people with chronic back pain and to explore the role of a specific service in an NHS pain clinic.
UWE researcher Dr Stuart McClean will be working in collaboration with Dr Lesley Wye from the University of Bristol, health practitioners at St Michaels Hospital and STAT Alexander Technique teachers. Stuart explains that "The Alexander Technique uses hands-on to achieve greater ease and poise by removing unhelpful habits that get in the way of simple activities such as sitting, standing and walking. It is all about self management and awareness."
He said, "Alexander Technique teaches people first to pause and to realise that there are conscious choices in everyday activities such as raising a hand, talking, using a computer, or playing a musical instrument.
"Alexander Technique requires significant effort on the part of the patient, which the AT teacher calls the 'student'. For this reason the long term impact can be very positive. Once the technique is learned the 'student' can use and practice the technique themselves, emphasising self management so that the effects may last after any formal treatment.
The University of Bristol and University of Southampton carried out a randomised controlled trial, published in the BMJ, and found AT was both clinically and cost effective for the treatment of back pain in primary care. This research builds on that study, as the researchers are looking at what happens when AT is introduced into a hospital pain clinic.
A participant in the trial, Ann Downton, 67, said, "I suffered from arthritis in the spine. I began to feel the benefits after four or five lessons. If you want something other than tablets to help back pain it can be expensive. Alexander Technique should be available on the NHS."
Dr Peter Brook, the lead consultant at St. Michael's Pain Clinic in Bristol, said, "I was very interested to read the BMJ study on back pain. The results are very encouraging and something that should be further evaluated to ascertain its value in chronic back pain. The general ethos of the Pain Management Service at University Hospitals Bristol is to encourage a self management approach to long term conditions. If lessons in the Alexander Technique can contribute to this it will be a useful addition to our range of treatment options."
If the study demonstrates that the AT service is feasible, acceptable, cost-neutral and beneficial to service users the AT teachers will seek longer term funding to extend the service. The findings will also be disseminated widely within the local Primary Care Trust and nationally at conferences. Stuart McClean concludes, "This is a scoping study but it could have a significant impact if AT is seen to demonstrate clear benefits to the service users as well as the NHS. The results will be widely published and used to inform future decisions surrounding pain management."
Provided by University of the West of England
Study: Patients often don't report pain
Study results raise questions about vertebroplasty for osteoporotic spinal compression fractures
Minimally Invasive Technique May Offer Quick and Safe Pain Relief in Osteoporosis Patients with Low Back Pain
Chronic pain should be considered a disease
Helping back pain sufferers to stay in work
How best to treat chronic pain? The jury is still out
Carbon monoxide deaths soar during power outages
CRISPR-Cas13 technique targets proteins causing ALS and Huntington's disease in the mouse nervous system
Energizing the brain: Combating worker fatigue using wearable neurotechnology


Today's economy is one that is connected to a degree never before seen. The wealth of information, products, and services available to anyone with an internet connection is truly incredible. BigCommerce understands the vast potential that the web represents. That's why they've designed a platform to help businesses manage their e-commerce websites and capture some of that potential. Their services help entrepreneurs and businesses create custom websites with amazing features. Subscribers get Google SEO optimization, design services, check out options, and more.
BigCommerce offers an easy to use, professionally designed platform. It's aimed to deliver e-commerce capabilities to anyone, anywhere. The technology may be sophisticated, but the inspiration for its development is not. Their mission is to connect more buyers and sellers around the world in an attempt to drive the development of new products and new methods of interaction.
Do you have a product that you think people might love? Would you like a way to expand the reach of a product you've already developed? A platform from BigCommerce might be your ticket to starting a new business or taking an existing one to the next level. Designed for beginners and experienced website owners, BigCommerce is always there to help.
There is one trick that we would recommend to anyone signing up with BigCommerce. Decide on a standard, plus, pro, or enterprise account and pay annually. You'll save 10% instantly on your BigCommerce subscription. It may seem like a lot at the time but why not keep your hard earned money? If you're not entirely sold on the service yet, try BigCommerce free for 15 days to make sure you're happy with their platform.
Speak with a consultant at BigCommerce by calling 1-866-581-4549 to learn more about the benefits of their platform. Follow them on Facebook, Twitter, and Pinterest to stay connected and learn more about the world of e-commerce. When you're ready to give it a try, use a coupon code to sign up and enjoy seeing your business succeed with the help of BigCommerce!
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text:
Tag: Nicolas Cage
Episode 134b: The Eclectic Mayhem Raids Again! Part 2
http://media.blubrry.com/xanaducinema/p/xanaducinema.com/upload/podcast-xanadu/xanadu0134b.mp3
Dear listeners, we are back with tales and movies from Day 2 of Fantastic Fest 2019! Alpacas are the way of the future!
Show notes below the cut!
Color Out of Space, Deerskin, H. P. Lovecraft, Ian Hodgkinson, Jean Dujardin, Justin Long, Nail in the Coffin, Nicolas Cage, Night Has Come, Quentin Dupieux, Richard Stanley, Son of the White Mare, The Antenna, The Vast of Night, The Wave, Vampiro
Episode 133c: Viva la Eclectic Mayhem! Part 3
http://media.blubrry.com/xanaducinema/p/xanaducinema.com/upload/podcast-xanadu/xanadu0133c.mp3
We return with tales from Day 3 of Fantastic Fest 2018! Nazi zombies, ghost boats, and Nicolas Cage ahoy!
Between Worlds, Drug Stories, Folklore, Franke Potente, Hold the Dark, Jeffrey Wright, Jeremy Saulnier, Joe Azzopardi, Jovan Adepo, Level 16, Maria Pulera, Nicolas Cage, Overlord, Slut in a Good Way, The Boat, The Trip Back, Winston Azzopardi
Episode 132c: Resurrection of the Eclectic Mayhem: Part 3
We return today with even more stories from Fantastic Fest 2017! Join us for the third installment in our six-episode series about all the films we saw, and check in tomorrow for the next installment!
Blade of the Immortal, Brawl in Cell Block 99, Brian Taylor, Downsizing, Fantastic Fest, Jailbreak, Mom & Dad, Nicolas Cage, Selma Blair, Takashi Miike, The Drop-In, Vince Vaughn
Episode 218: A Nickel Short of Quasimodo
This week, dear listeners, we bring you an episode about Abel Ferrara's 1992 movie Bad Lieutenant and Werner Herzog's 2009 not-remake Bad Lieutenant: Port of Call New Orleans! Why? Well, Windy hadn't seen them before, and Melissa had a need to talk about inexplicable iguanas.
A special note about this episode: it was recorded over a year ago, on February 23rd, 2016. It is one of six episodes that we recorded early last year, which never got posted due to our later hiatus and subsequent posting schedule change. Thankfully, we were talking about movies that were already over five years old, so there aren't too many stale references.
If you would like to drink along, we recommend either Two Gingers whiskey or New Glarus Staghorn Octoberfest beer.
Abel Ferrara, Bad Lieutenant, Bad Lieutenant: Port of Call New Orleans, Fairuza Balk, Harvey Keitel, Jennifer Coolidge, Michael Shannon, Nicolas Cage, Val Kilmer, Werner Herzog, Where the Sidewalk Ends, Zoe Lund
Episode 119d: The Return of the Eclectic Mayhem: Part 4
, with special guest: Barbara Kennedy, Leigh Harris, Susana Soliz
http://media.blubrry.com/xanaducinema/p/xanaducinema.com/upload/podcast-xanadu/xanadu0119d.mp3
We're still alive, dearest listeners, so we bring you our episode about Day 4 of Fantastic Fest 2016!
Once again, we recorded from inside the festival, so you will hear plenty of background noise during this recording. Crowds, cars, and interruptions ahoy!
Voices heard:
Susana Soliz
Barbara Kennedy
Leigh Harris
Movies mentioned:
Salt and Fire
Zodiac Killer
Dearest Sister
Chanthaly
Dog Eat Dog
Sadako vs. Kayako
People mentioned:
Mattie Do
Barbara Kennedy, Chanthaly, Dearest Sister, Dog Eat Dog, Fantastic Fest, Fashionista, Fraud, Khal Nayak, Leigh Harris, Mattie Do, Michael Shannon, Miss Peregrine's Home for Peculiar Children, Nicolas Cage, Paul Schrader, Red White and Blue, Sadako vs. Kayako, Salt and Fire, Susana Soliz, Tim Burton, Werner Herzog, Willem Dafoe, Zodiac Killer


A cream pure silk saree from Bishnupur with green-blue-brown kalamkari-styled printed patterns on the borders, pleats and pallav. The colourful patterns form a lovely contrast with the plain, cream body. The elegant colours, light texture and pretty patterns define the charm of this soft, silk saree. It comes with a matching, kalamkari printed blouse piece.


Regardless of the weather outside, the calendar tells me it's Spring. Seems like a good time to get some items in front of you.
Schedule: This week we will have the final Thursday Abide gathering at 12pm, including some time around communion together. These have been small, but wonderful times of pause in our week for refreshing in the Word, worship and prayer. Holy Week gatherings include our Good Friday service (7:30pm), Easter Sunrise (6:30am at Moore Park) and Easter Celebrations (9:00 and 10:35am). Celebration Choir has 3 more Sundays until summer break (4/24, 5/22 and 6/12).
Sermon Series: We will be moving forward deeper into the book of James throughout this spring and early summer. I hope your heart is being challenged about what it is to live a life of endurance and joy, in light of the Father's loving, merciful care and direction.
WA Year –End Gathering at Moore Park: Sunday, June 12 we will be hosting a year-end WA gathering at Moore Park right after church. Please mark your calendars for what will be a very fun time of reflection, celebration and food. It's time to gather and enjoy what God has done in and through us this past year. More details to follow.
From time to time we see significant transitions in our WA family. Let me bring a couple to your attention for prayer and connection. First, after nearly five years of very faithful service, Julie Martin will be leaving her position as WA administrator. She has been a core member of the inner-workings of our ministry and a huge benefit to me in this first season at EFC. Julie has been praying for some time whether this was the best long-term place of service, and is sensing some other opportunities ahead as the Lord leads. Please make sure to thank her for all that she has done to help this ministry run smoothly and with great impact through the years. We will miss her on the staff side of things, but thankfully get to continue worshiping alongside her on the worship teams.
Secondly, Zach Brenner will be entering into a full-time WA internship beginning June 1. Please be praying for him as he embarks on a season of development and discernment with regards to his sense of calling into Worship ministry.
Lastly, Allison Greenwald has taken on the key volunteer role of Faith Story Coordinator for the next ministry year. We are very excited to have her jump into this position and help grow this very important facet of our gatherings. I am always so encouraged by hearing what God is doing in and through other folks and looking forward to more of this under Allison's leadership.
Last Thoughts: This week brings us to the focal point of the Christian Year. Whether you are someone who has leaned into the rhythm of the church seasons or not, we have a significant opportunity to pause, deepen and celebrate the life-changing reality of Jesus as we walk through these next few days.
My prayer for all of us is that we become more keenly aware of the suffering and sacrifice of the Cross, resulting in a greater celebration of the power of the resurrection.


For those willing to travel, Nåttarö takes over 2 hours from central station and is almost the furthest south you can travel in the Stockholm archipelago. Mostly made up of thick forest, this island is maybe perfect for hikers but is also perfect for sunbathers too.
From the ferry port, the walk immediately in front of you will take you to Storsand (Big Sand), an aptly named beach that is both big and sandy. Many who visit this island will go directly here and if you are going to be one of them, you'll pass by the Nåttarö pub and restaurant and summer houses. Towards the beach, you will also spot a cave.
To the north, there is another swimming spot and to the south there are small lakes and nature trails.
Note: We are yet to explore this island properly so this review will be updated.


Running a great shelter program is a tough job. Our Shelter Managers Learning Network is a place to get support and peer learning opportunities for those who are responsible for or assist in managing shelter programs. We discuss the complexities of shelter management and support each other in strengthening management and leadership skills. If you're interested in joining the network, email deadria@wscadv.org.
There are currently no upcoming events for Shelter Support.


The College of Arts and Sciences offers an interdisciplinary minor in environmental studies. This minor is nontechnical in nature and is specifically designed for students seeking the Bachelor of Arts degree. Its focus is on the interaction of humans and the natural environment and the consequences of that interaction.
The environmental studies minor does not seek to train professional environmentalists, but in combination with existing major programs it will give the student a broad foundation for more advanced environmental studies programs, professional work in law, regional planning or resource management, various environmental positions in government, business, or teaching. The plan will also provide students with a better understanding of basic ecology and the nature of environmental problems so that they can make more knowledgeable value judgments on environmental issues, a vital concern in the contemporary world.


Here is a quick follow-up to our post on China called Fresh signs of China's asset bubble. Looks like the Chinese government is taking this potential bubble issue quite seriously. They've been forcing banks to significantly cut lending to reduce all the liquidity making it's way through their economy, manifesting itself in asset inflation. People's Bank of China even issued a low yielding note and forced some banks to buy it to limit cash on their balance sheets.
This reduction in liquidity is showing up in the stock market as well.
This was a timely and prudent move on China's behalf, as the asset bubble was clearly beginning to build. Let's see if they can maintain the discipline going forward.


text:
\section{Introduction}
English has an abundance of labeled data that can be used for various Natural Language Processing (NLP) tasks, such as part-of-speech tagging (POS), named entity recognition (NER), and natural language inference (NLI). This richness of labeled data manifests itself as a boost in accuracy in the current era of data-hungry deep learning algorithms.
However, the same is not true for many other languages where task specific data is scarce and expensive to acquire. This motivates the need for cross-lingual transfer learning -- the ability to leverage the knowledge from task specific data available in one or more languages to solve that task in languages with little or no task-specific data.

Recent progress in NMT has enabled one to train multilingual systems that support translation from multiple source languages into multiple target languages within a single model~\cite{firat2016multi,johnson2017google,aharoni2019massively}. Such multilingual NMT (mNMT) systems often demonstrate large improvements in translation quality on low resource languages. This positive transfer originates from the model's ability to learn  representations which are transferable across languages. Previous work has shown that these representations can then be used for cross-lingual transfer in other downstream NLP tasks - albeit on only a pair of language pairs~\cite{eriguchi2018zero}, or by limiting the decoder to use a pooled vector representation of the entire sentence from the encoder \cite{artetxe2018massively}.

In this paper we scale up the number of translation directions used in the NMT model to include 102 languages to and from English. Unlike \citet{artetxe2018massively}, we do not apply any restricting operations such as pooling while training mNMT which allows us to obtain token level representations making it possible to transfer them to sequence tagging tasks as well. We find that mNMT models trained using plain translation losses can out of the box emerge as competitive alternatives to other methods at the forefront of cross-lingual transfer learning \cite{devlin2018bert,artetxe2018massively}

Our contributions in this paper are threefold:
\begin{itemize}
\item We use representations from a Massively Multilingual Translation Encoder (MMTE) that can handle 103 languages to achieve cross-lingual transfer on 5 classification and sequence tagging tasks spanning more than 50 languages.
\item We compare MMTE to mBERT in different cross-lingual transfer scenarios including zero-shot, few-shot, fine-tuning, and feature extraction scenarios.
\item We outperform the state-of-the-art on zero-shot cross-lingual POS tagging [Universal Dependencies 2.3 dataset \cite{nivre2018universal}], intent classification \cite{schuster2018cross}, and achieve results comparable to state-of-the-art on document classification [ML-Doc dataset \cite{SCHWENK18.658}].
\end{itemize}

The remainder of this paper is organized as follows. Section \ref{sec: mmte} describes our MMTE model in detail and points out its differences from mBERT. All experimental details, results and analysis are given in Sections \ref{sec: results} and \ref{sec: analysis}. This is followed by a discussion of related work. In Section~\ref{conclusion}, we summarize our findings and present directions for future research. We emphasize that the primary motivation of the paper is not to challenge the state-of-the-art but instead to investigate the effectiveness of representations learned from an mNMT model in various transfer-learning settings.

\section{Massively Multilingual Neural Machine Translation Model} \label{sec: mmte}

In this section, we describe our massively multilingual NMT system. Similar to BERT, our transfer learning setup has two distinct steps: \textit{pre-training} and \textit{fine-tuning}. During pre-training, the NMT model is trained on large amounts of parallel data to perform translation.  During fine-tuning, we initialize our downstream model with the pre-trained parameters from the encoder of the NMT system, and then all of the parameters are fine-tuned using labeled data from the downstream tasks.

\subsection{Model Architecture}
We train our Massively Multilingual NMT system using the Transformer architecture \citep{vaswani2017attention} in the open-source implementation under the Lingvo framework \citep{lingvo}. We use a larger version of Transformer Big containing 375M parameters (6 layers, 16 heads, 8192 hidden dimension) \citep{chen-EtAl:2018:Long1}, and a shared source-target sentence-piece model (SPM)\footnote{https://github.com/google/sentencepiece} \cite{kudo2018sentencepiece} vocabulary with 64k individual tokens. All our models are trained with Adafactor \citep{shazeer2018adafactor} with momentum factorization, a learning rate schedule of (3.0, 40k)\footnote{The shorthand form (3.0, 40k) corresponds to a learning rate of 3.0, with 40k warm-up steps for the schedule, which is decayed with the inverse square root of the number of training steps after warm-up.} and a per-parameter norm clipping threshold of 1.0. The encoder of this NMT model comprises approximately 190M parameters and is subsequently used for fine-tuning.

\subsection{Pre-training}

\paragraph{Objective}
We train a massively multilingual NMT system which is capable of translating between a large number of language pairs at the same time by optimizing the translation objective between language pairs. To train such a multilingual system within a single model, we use the strategy proposed in \cite{johnson2017google} which suggests prepending a target language token to every source sequence to be translated. This simple and effective strategy enables us to share the encoder, decoder, and attention mechanisms across all language pairs.

\paragraph{Data}
We train our multilingual NMT system on a massive scale, using an in-house corpus generated by crawling and extracting parallel sentences from the web \cite{uszkoreit2010large}. This corpus contains parallel documents for 102 languages, to and from English, comprising a total of 25 billion sentence pairs. The number of parallel sentences per language in our corpus ranges from around 35 thousand to almost 2 billion. Figure \ref{fig:data} illustrates the data distribution for all 204 language pairs used to train the NMT model. Language ids for all the languages are also provided in supplementary material.

\begin{figure}[t!]
\begin{center}
\includegraphics[scale=0.35]{data_dist.png}
\caption{Per language pair data distribution of the dataset used for our multilingual NMT model. The y-axis depicts the number of training examples available per language pair on a logarithmic scale. Dataset sizes range from 35k for the lowest resource language pairs to 2 billion for the largest.}
\label{fig:data}
\end{center}
\end{figure}

\paragraph{Data sampling policy}
Given the wide distribution of data across language pairs, we used a temperature based data balancing strategy. For a given language pair, $l$, let $D_l$ be the size of the available parallel corpus. Then if we adopt a naive strategy and sample from the union of the datasets, the probability of the sample being from language pair $l$ will be $p_l=\frac{D_l}{\Sigma_lD_l}$. However, this strategy would starve low resource language pairs. To control for the ratio of samples from different language pairs, we sample a fixed number of sentences from the training data, with the probability of a sentence belonging to language pair $l$ being proportional to $p_l^{\frac{1}{T}}$, where $T$ is the sampling temperature. As a result, $T=1$ would correspond to a true data distribution, and, $T=100$ yields an (almost) equal number of samples for each language pair (close to a uniform distribution with over-sampling for low-resource language-pairs). We set $T=5$ for a balanced sampling strategy. To control the contribution of each language pair when constructing the vocabulary, we use the same temperature based sampling strategy with $T=5$. Our SPM vocabulary has a character coverage of $0.999995$.

\paragraph{Model quality}
We use BLEU score~\cite{papineni2002bleu} to evaluate the quality of our translation model(s). Our mNMT model performs worse than the bilingual baseline on high resource language pairs but improves upon it on low resource language pairs. The average drop in BLEU score on 204 language pairs as compared to bilingual baselines is just 0.25 BLEU. This is impressive considering we are comparing one multilingual model to 204 different bilingual models. Table \ref{bleuscore table} compares the BLEU scores achieved by mNMT to that of the bilingual baselines on 10 representative language pairs.\footnote{We chose a diverse set of language pairs with varying language families, scripts, and dataset sizes.} These scores are obtained on an internal evaluation set which contains around 5k examples per language pair.

\begin{table}[]
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}{c|cc|c|cc}
\toprule
\multicolumn{1}{l}{} & \multicolumn{1}{l}{baseline*} & \multicolumn{1}{l}{mNMT} & \multicolumn{1}{l}{} & \multicolumn{1}{l}{baseline*} & \multicolumn{1}{l}{mNMT} \\
\midrule
ur-en & \textbf{27.84} & 27.24          & en-bg & \textbf{31.31} & 29.36          \\
mr-en & 27.81          & \textbf{28.61} & en-es & \textbf{35.23} & 34.35          \\
be-en & 24.23          & \textbf{24.66} & en-sw & 18.73          & \textbf{19.79} \\
ru-en & 26.54          & \textbf{26.56} & en-pt & 37.19          & \textbf{37.41} \\
fr-en & \textbf{37.49} & 34.02          & en-hi & 16.46          & \textbf{16.63}  \\
\end{tabular}}
\caption{BLEU scores on ten language pairs with mNMT. *baseline refers to a transformer based bilingual NMT model trained on only one language pair.} \label{bleuscore table}
\end{table}

\subsection{Fine-tuning mNMT Encoder}
Fine-tuning involves taking the encoder of our mNMT model, named Massively Multilingual Translation Encoder (MMTE), and adapting it to the downstream task. For tasks which involve single input, the text is directly fed into the encoder. For tasks such as entailment which involve input pairs, we concatenate the two inputs using a separator token and pass this through the encoder. For each downstream task, the inputs and outputs are passed through the encoder and we fine-tune all the parameters end-to-end. The encoder encodes the input through the stack of Transformer layers and produces representations for each token at the output. For sequence tagging tasks, these token level representations are individually fed into a task-specific output layer. For classification or entailment tasks, we apply max-pooling on the token level representations and feed this into the task-specific output layer.

It should be noted that fine-tuning is relatively inexpensive and fast. All of the results can be obtained within a few thousand gradient steps. The individual task-specific modeling details are described in detail in section \ref{sec: results}.
It is also important to note that while the encoder, the attention mechanism, and the decoder of the model are trained in the pre-training phase, only the encoder is used during fine-tuning.

\subsection{Differences with mBERT}
We point out some of the major difference between mBERT and MMTE are:
\begin{itemize}
\item mBERT uses two unsupervised pre-training objectives called masked language modeling (MLM) and next sentence prediction (NSP) which are both trained on monolingual data in 104 languages. MMTE on the other hand uses parallel data in 103 languages (102 languages to and from English) for supervised training with negative log-likelihood as the loss. It should be noted that mBERT uses clean Wikipedia data while MMTE is pre-trained on noisy parallel data from the web.
\item mBERT uses 12 transformer layers, 12 attention heads, 768 hidden dimensions and has 178M parameters while MMTE uses 6 transformer layers, 16 attention heads, and 8196 hidden dimensions with 190M parameters. Note that, the effective capacity of these two models cannot easily be compared by simply counting number of parameters, due to the added characteristic complexity with depth and width.
\item MMTE uses SPM to tokenize input with 64k vocabulary size while mBERT uses a Wordpiece model \citep{wu2016google} with 110k vocabulary size.
\end{itemize}

\section{Experiments and Results} \label{sec: results}
As stated earlier, we use MMTE to perform downstream cross-lingual transfer on 5 NLP tasks. These include 3 classification tasks: NLI (XNLI dataset), document classification (MLDoc dataset) and intent classification, and 2 sequence tagging tasks: POS tagging and NER. We detail all of the experiments in this section.

\subsection{XNLI: Cross-lingual NLI}
XNLI is a popularly used corpus for evaluating cross-lingual sentence classification. It contains data in 15 languages \cite{conneau2018xnli}. Evaluation is based on classification accuracy for pairs of sentences as one of entailment, neutral, or contradiction. We feed the text pair separated by a special token into MMTE and add a small network on top of it to build a classifier. This small network consists of a pre-pool feed-forward layer with 64 units, a max-pool layer which pools word level representations to get the sentence representation, and a post-pool feed-forward layer with 64 units. The optimizer used is Adafactor with a learning rate schedule of (0.2, 90k). The classifier is trained on English only and evaluated on all the 15 languages. Results are reported in Table \ref{tab:xnli}. Please refer to Appedix Table 1 for language names associated with the codes.

\begin{table}[h]
\centering
\resizebox{.7\linewidth}{!}{
\begin{tabular}{c|cc|c}
\toprule
    & mBERT*         & MMTE          & SOTA$^\dagger$ \\
\midrule
en  & \textbf{82.1} & 79.6          & 85.0 \\
ar  & 64.9          & \textbf{64.9} & 73.1 \\
bg  & 68.9          & \textbf{70.4} & 77.4 \\
de  & \textbf{71.1} & 68.2          & 77.8 \\
el  & 66.4          & \textbf{67.3} & 76.6 \\
es  & \textbf{74.3} & 71.6          & 78.9 \\
fr  & \textbf{73.8} & 69.5          & 78.7 \\
hi  & 60.0          & \textbf{63.5} & 69.6 \\
ru  & \textbf{69.0} & 66.2          & 75.3 \\
sw  & 50.4          & \textbf{61.9} & 68.4 \\
th  & 55.8          & \textbf{66.2} & 73.2 \\
tr  & 61.6          & \textbf{63.6} & 72.5 \\
ur  & 58.0          & \textbf{60.0} & 67.3 \\
vi  & 69.5          & \textbf{69.7} & 76.1 \\
zh  & \textbf{69.3} & 69.2          & 76.5 \\
\midrule
avg & 66.3          & \textbf{67.5} & 75.1 \\
\bottomrule
\end{tabular}}
\caption{Accuracies on the test set of the XNLI dataset. *mBERT numbers have been taken from \citet{wu2019beto}. $\dagger$ SOTA in the last column refers to the MLM + translation language modeling (TLM) results reported in \citet{lample2019cross}.} \label{tab:xnli}
\end{table}

MMTE outperforms mBERT on 9 out of 15 languages and by 1.2 points on average. BERT achieves excellent results on English, outperforming our system by 2.5 points but its zero-shot cross-lingual transfer performance is weaker than MMTE. We see most gains in low resource languages such as ar, hi, ur, and sw. MMTE however falls short of the current state-of-the-art (SOTA) on XNLI~\citep{lample2019cross}. We hypothesize this might be because of 2 reasons: (1) They use only the 15 languages associated with the XNLI task for pre-training their model, and (2) They use both monolingual and parallel data for pre-training while we just use parallel data. We confirm our first hypothesis later in Section~\ref{sec: analysis} where we see that decreasing the number of languages in mNMT improves the performance on XNLI.

\subsection{MLDoc: Document Classification}
MLDoc is a balanced subset of the Reuters corpus covering 8 languages for document classification \citep{SCHWENK18.658}. This is a 4-way classification task of identifying topics between CCAT (Corporate/Industrial), ECAT (Economics), GCAT (Government/Social), and MCAT (Markets). Performance is evaluated based on classification accuracy. We split the document using the sentence-piece model and feed the first 200 tokens into the encoder for classification. The task-specific network and the optimizer used is same as the one used for XNLI. Learning rate schedule is (0.2,5k). We perform both in-language and zero-shot evaluation. The in-language setting has training, development and test sets from the language. In the zero-shot setting, the train and dev sets contain only English examples but we test on all the languages. The results of both the experiments are reported in Table \ref{tab:mldoc}.

\begin{table}[h]
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}{c|cc|cc|c}
\toprule
    & \multicolumn{2}{c|}{In language} & \multicolumn{3}{c}{Zero-shot} \\
\midrule
    & mBERT*          & MMTE          & mBERT*  & MMTE         & SOTA$^\dagger$ \\
\midrule
en  & 94.2           & \textbf{94.7}  & 94.2   & \textbf{94.7} & 89.9 \\
de  & 93.3           & \textbf{93.4}  & \textbf{80.2}   & 77.4          & 84.8 \\
zh  & 89.3           & \textbf{90.0}  & \textbf{76.9}   & 73.4          & 71.9 \\
es  & \textbf{95.7}  & 95.6           & 72.6   & \textbf{73.0} & 77.3 \\
fr  & \textbf{93.4}  & 92.7           & 72.6   & \textbf{77.2} & 78.0 \\
it  & \textbf{88.0}  & 87.6           & \textbf{68.9}   & 64.2          & 69.4 \\
ja  & \textbf{88.4}  & 88.1           & 56.5   & \textbf{69.0} & 60.3 \\
ru  & \textbf{87.5}  & 87.4           & \textbf{73.7}   & 68.9          & 67.8 \\
\midrule
avg & \textbf{91.2}  & 91.2           & 74.5   & \textbf{74.7} & 74.9 \\
\bottomrule
\end{tabular}}
\caption{Accuracies on the test set of the MLDoc dataset.  *mBERT numbers have been taken from \citet{wu2019beto}. mBERT is also the state-of-the-art for in-language training. $\dagger$ For zero-shot SOTA refers to \citet{artetxe2018massively}. \label{tab:mldoc}}
\end{table}

MMTE performance is on par with mBERT for in-language training on all the languages. It slightly edges over mBERT on zero-shot transfer while lagging behind SOTA by 0.2 points. Interestingly, MMTE beats SOTA on Japanese by more than 8 points. This may be due to the different nature and amount of data used for pre-training by these methods.

\subsection{Cross-lingual Intent Classification}
\citet{schuster2018cross} recently presented a dataset for multilingual task oriented dialog. This dataset contains 57k annotated utterances in English (43k), Spanish (8.6k), and Thai (5k) with 12 different intents across the domains weather, alarm, and reminder. The evaluation metric used is classification accuracy. We use this data for both in-language training and zero-shot transfer.  The task-specific network and the optimizer used is the same as the one used for the above two tasks. The learning rate schedule is (0.1,100k). Results are reported in Table \ref{tab:clic}. MMTE outperforms both mBERT and previous SOTA in both in-language and zero-shot setting on all 3 languages and establishes a new SOTA for this dataset.

\begin{table}[h]
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}{c|ccc|ccc}
\toprule
    & \multicolumn{3}{c|}{In language} & \multicolumn{3}{c}{Zero-shot} \\
\midrule
    & mBERT*     & MMTE     & SOTA$^\dagger$    & mBERT*    & MMTE    & SOTA$^\dagger$    \\
\midrule
en  & 99.3      & \textbf{99.4}      & 99.1    & 99.3     & \textbf{99.4}     & 99.1    \\
es  & 98.4       & \textbf{98.8}      & 98.6    & 69.2      & \textbf{93.6}     & 85.4    \\
th  & 97.0       & \textbf{97.6}      & 97.4    & 43.4      & \textbf{89.6}     & 95.9    \\
\midrule
avg & 98.2       & \textbf{98.6}      & 98.4    & 70.6      & \textbf{94.2}     & 93.5    \\
\bottomrule
\end{tabular}}
\caption{Accuracies on the test set of the intent classification dataset. *mBERT numbers are from our own implementation using the publicly available mBERT checkpoint. $\dagger$ SOTA refers to the numbers reported in \citet{schuster2018cross}.} \label{tab:clic}
\end{table}
\subsection{POS Tagging}
We use universal dependencies POS tagging data from the Universal Dependency v2.3 \citep{nivre2018universal,zeman2018conll}. Gold segmentation is used for training, tuning and testing. The POS tagging task has 17 labels for all languages. We consider 48 different languages. These languages are chosen based on intersection of languages for which POS labels are available in the universal dependencies dataset and the languages supported by our mNMT model. The task-specific network consists of a one layer feed-forward neural network with 784 units. Since MMTE operates on the subword-level, we only consider the representation of the first subword token of each word. The optimizer used is Adafactor with learning rate schedule (0.1,40k). The evaluation metric used is F1-score, which is same as accuracy in our case since we use gold-segmented data. Results of both in-language and zero-shot setting are reported in Table \ref{tab:posr}.

While mBERT outperforms MMTE on in-language training by a small margin of 0.16 points, MMTE beats mBERT by nearly 0.6 points in the zero-shot setting. Similar to results in XNLI, we see MMTE outperform mBERT on low resource languages. Since mBERT is SOTA for zero-shot cross-lingual transfer on POS tagging task \cite{wu2019beto}, we also establish state-of-the-art on this dataset by beating mBERT in this setting.

\begin{table}[h]
\centering
\resizebox{.9\linewidth}{!}{
\begin{tabular}{c|cc|cc}
\toprule
    & \multicolumn{2}{c|}{In language} & \multicolumn{2}{c}{Zero-shot} \\
\midrule
    & mBERT*          & MMTE          & mBERT*         & MMTE         \\
\midrule
ar  & \textbf{97.25} & 96.72          & 58.67          & \textbf{66.87} \\
bg  & 98.86          & \textbf{98.93} & 86.65          & \textbf{87.61} \\
de  & \textbf{96.21} & 95.92          & \textbf{91.63} & 89.22          \\
en  & 96.11          & \textbf{96.34} & 96.16          & \textbf{96.34} \\
es  & 97.70          & \textbf{97.79} & \textbf{87.56} & 85.11          \\
fr  & \textbf{97.87} & 97.83          & \textbf{89.05} & 86.22          \\
hi  & 97.39          & \textbf{97.53} & 72.00          & \textbf{74.48} \\
mr  & 84.15          & \textbf{85.10} & 60.23          & \textbf{68.40} \\
nl  & \textbf{97.82} & 97.27          & \textbf{90.30} & 88.40          \\
pt  & \textbf{98.04} & 97.74          & \textbf{90.33} & 88.60          \\
ro  & \textbf{97.52} & 97.43          & \textbf{79.44} & 78.80          \\
sv  & \textbf{97.35} & 96.92          & \textbf{91.60} & 89.95          \\
te  & 93.66          & \textbf{93.73} & 81.12          & \textbf{83.40} \\
ur  & \textbf{94.43} & 94.42          & 65.88          & \textbf{69.36} \\
zh  & \textbf{96.25} & 95.87          & 67.38          & \textbf{67.51} \\
\midrule
48L & \textbf{95.65} & 95.49          & 74.87          & \textbf{75.43} \\
\bottomrule
\end{tabular}}
\caption{F1 scores on the dev set of the POS tagging dataset. *mBERT numbers are from our own implementation using the publicly available mBERT checkpoint. For readability, we only report results on 15 select languages and the 48 Language average (48L).} \label{tab:posr}
\end{table}
\subsection{Named Entity Recognition}
For NER, we use the dataset from the CoNLL 2002 and 2003 NER shared tasks, which when combined have 4 languages \cite{DBLP:journals/corr/cs-CL-0209010,sang2003introduction}. The labeling scheme is IOB with 4 types of named entities. The task-specific network, optimizer, and the learning rate schedule is the same as in the setup for POS tagging. The evaluation metric is span-based F1. Table \ref{tab:nerr} reports the results of both in-language and zero-shot settings.

MMTE performs significantly worse than mBERT on the NER task in all languages. On average, mBERT beats MMTE by 7 F1 points in the in-language setting and by more than 18 points in the zero-shot setting. We hypothesize that this might be because of two reasons: (1) mBERT is trained on clean Wikipedia data which is entity-rich while MMTE is trained on noisy web data with fewer entities, and (2) the translation task just copies the entities from the source to the target and therefore might not be able to accurately recognize them. This result points to the importance of the type of pre-training data and objective on down-stream task performance. We plan to investigate this further in future work.

\begin{table}[h]
\centering
\resizebox{.9\linewidth}{!}{
\begin{tabular}{c|cc|cc}
\toprule
    & \multicolumn{2}{c|}{In language} & \multicolumn{2}{c}{Zero-shot} \\
\midrule
    & mBERT*              & MMTE      & mBERT*             & MMTE     \\
\midrule
en  & \textbf{92.0}      & 87.8       & \textbf{92.0}     & 87.8      \\
es  & \textbf{87.4}      & 78.6       & \textbf{75.0}     & 52.0      \\
de  & \textbf{82.8}      & 78.7       & \textbf{69.6}     & 51.8      \\
nl  & \textbf{90.9}      & 81.7       & \textbf{77.6}     & 50.8      \\
\midrule
avg & \textbf{88.3}      & 81.7       & \textbf{78.5}     & 60.6     \\
\bottomrule
\end{tabular}}
\caption{F1 scores on the test set of the NER task. *mBERT numbers have been taken from \citet{wu2019beto}. mBERT is also the state-of-the-art for zero-shot setting.} \label{tab:nerr}
\end{table}

\section{Analysis} \label{sec: analysis}
In this section, we consider some additional settings for comparing mBERT and MMTE. We also investigate the impact of the number of languages and the target language token on MMTE performance.

\paragraph{Feature-based Approach}
In this setting, instead of fine-tuning the entire network of mBERT or MMTE, we only fine-tune the task-specific network which only has a small percentage of the total number of parameters. The rest of the model parameters are frozen. We perform this experiment on POS tagging task by fine-tuning a single layer feed-forward neural network stacked on top of mBERT and MMTE. We report the results in Table \ref{tab:febert}. While the scores of the feature-based approach are significantly lower than those obtained via full fine-tuning (\ref{tab:posr}), we see that MMTE still outperforms mBERT on both in-language and zero-shot settings by an even bigger margin. This is particularly interesting as the feature-based approach has its own advantages: 1) it is applicable to downstream tasks which require significant task-specific parameters on top of a transformer encoder, 2) it is computationally cheaper to train and tune the downstream model, and 3) it is compact and scalable since we only need a small number of task-specific parameters.

\begin{table}[h]
\centering
\resizebox{.9\linewidth}{!}{
\begin{tabular}{c|cc|cc}
\toprule
    & \multicolumn{2}{c|}{In language} & \multicolumn{2}{c}{Zero-shot} \\
\midrule
    & mBERT          & MMTE          & mBERT         & MMTE         \\
\midrule
ar   & 87.65          & \textbf{91.50} & \textbf{61.08} & 60.35          \\
bg   & 95.82          & \textbf{96.72} & \textbf{88.08} & 83.48          \\
de   & 90.68          & \textbf{93.34} & 85.10          & \textbf{89.52} \\
en   & \textbf{92.73} & 92.15          & \textbf{92.73} & 92.33          \\
es   & \textbf{93.03} & 92.45          & 76.95          & \textbf{83.66} \\
fr   & 92.20          & \textbf{93.41} & 77.64          & \textbf{83.58} \\
hi   & 89.86          & \textbf{92.15} & 64.21          & \textbf{73.29} \\
mr   & 83.50          & \textbf{84.49} & 52.27          & \textbf{66.36} \\
nl   & 92.87          & \textbf{94.00} & 86.38          & \textbf{87.32} \\
pt   & 93.14          & \textbf{93.84} & 85.95          & \textbf{86.86} \\
ro   & \textbf{90.76} & 89.68          & 71.92          & \textbf{77.77} \\
sv   & 93.67          & \textbf{94.35} & 85.98          & \textbf{87.82} \\
te   & 90.57          & \textbf{92.75} & 70.09          & \textbf{81.12} \\
ur   & 90.02          & \textbf{90.32} & 58.98          & \textbf{67.90} \\
zh   & \textbf{86.29} & 84.34          & \textbf{60.82} & 56.50          \\
\midrule
avg  & 90.85          & \textbf{91.70} & 74.55          & \textbf{78.52} \\
\bottomrule
\end{tabular}}
\caption{F1 scores on dev set of POS tagging dataset using both mBERT and MMTE as feature extractor.} \label{tab:febert}
\end{table}

\paragraph{Few Shot Transfer}
While zero-shot transfer is a good measure of a model's natural cross-lingual effectiveness, the more practical setting is the few-shot transfer scenario as we almost always have access to, or can cheaply acquire, a small amount of data in the target language. We report the few-shot transfer results of mBERT and MMTE on the POS tagging dataset in \ref{tab:fews}. To simulate the few-shot setting, in addition to using English data, we use 10 examples from each language (upsampled to 1000). MMTE outperforms mBERT in few-shot setting by 0.6 points averaged over 48 languages. Once again, we see that the gains are more pronounced in low resource languages.

\begin{table}[h]
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}{c|cc|c|cc}
\toprule
   & mBERT          & MMTE           &     & mBERT          & MMTE           \\
\midrule
ar & 85.20          & \textbf{85.53} & nl  & 90.01          & \textbf{90.26} \\
bg & \textbf{93.27} & 92.13          & pt  & 92.18          & \textbf{92.29} \\
de & \textbf{92.24} & 91.57          & ro  & 84.58          & \textbf{84.68} \\
en & 96.07          & \textbf{96.29} & sv  & 91.61          & \textbf{92.11} \\
es & \textbf{92.52} & 92.12          & te  & 83.84          & \textbf{84.74} \\
fr & \textbf{92.54} & 91.12          & ur  & 81.38          & \textbf{82.94} \\
hi & 86.29          & \textbf{87.32} & zh  & \textbf{81.14} & 76.32          \\
mr & 66.59          & \textbf{79.32} & 48L & 83.97          & \textbf{84.58} \\
\bottomrule
\end{tabular}}
\caption{F1 scores on dev set of POS tagging dataset in few-shot setting using 10 examples from each language in addition to English data.} \label{tab:fews}
\end{table}

\paragraph{One Model for all Languages}
Another setting of importance is the in-language training where instead of training one model for each language, we concatenate all the data and train one model jointly on all languages. We perform this experiment on the POS tagging dataset with 48 languages and report results in Table \ref{tab:onemodel}. We observe that MMTE performance is on par with mBERT. We also find that the 48 language average improves by 0.2 points as compared to the one model per language setting in Table \ref{tab:posr}.

\begin{table}[h]
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}{c|cc|c|cc}
\toprule
   & mBERT          & MMTE           &     & mBERT          & MMTE           \\
\midrule
ar & \textbf{97.09} & 96.92          & nl  & \textbf{97.60} & 97.59          \\
bg & \textbf{98.83} & 98.94          & pt  & \textbf{98.42} & 98.02          \\
de & \textbf{95.94} & 95.68          & ro  & \textbf{97.48} & 97.47          \\
en & 96.04          & \textbf{96.28} & sv  & \textbf{97.27} & 97.22          \\
es & 97.71          & \textbf{97.77} & te  & \textbf{94.86} & 94.81          \\
fr & 97.74          & \textbf{97.86} & ur  & 94.39          & \textbf{94.77} \\
hi & 97.34          & \textbf{97.62} & zh  & \textbf{96.01} & 95.84          \\
mr & 84.70          & \textbf{86.13} & 48L & 95.67          & \textbf{95.69}  \\
\bottomrule
\end{tabular}}
\caption{F1 scores on dev set of POS tagging dataset in one model for all language setting.} \label{tab:onemodel}
\end{table}

\paragraph{Number of Languages in mNMT} We perform an ablation where we vary the number of languages used in the pre-training step. Apart from the 103 language setting, we consider 2 additional settings: 1) where we train mNMT on 4 languages to and from English, and 2) where we use 25 languages. The results are presented in Table \ref{tab:numlang}. We see that as we scale up the languages the zero-shot performance goes down on both POS tagging and XNLI tasks. These losses align with the relative BLEU scores of these models suggesting that the regressions are due to interference arising from the large number of languages attenuating the capacity of the NMT model. Scaling up the mNMT model to include more languages without diminishing cross-lingual effectiveness is a direction for future work.

\begin{table}[h]
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}{c|ccc|ccc}
\toprule
   & \multicolumn{3}{c|}{POS} & \multicolumn{3}{c}{XNLI} \\
\midrule
   & 4x4   & 25x25 & 102x102 & 4x4   & 25x25  & 102x102 \\
\midrule
en & 96.30 & 96.25 & 96.28   & 80.1  & 80.0   & 79.6    \\
es & 86.61 & ----  & 85.13   & 74.2  & ----   & 71.6    \\
fr & 86.62 & 85.81 & 85.23   & 72.9  & 72.1   & 69.5    \\
de & 90.52 & 89.84 & 88.57   & 71.4  & 71.1   & 68.2    \\
zh & ----  & 67.83 & 67.51   & ----  & 72.7   & 69.2    \\
\bottomrule
\end{tabular}}
\caption{Effect of number of languages used for mNMT training on downstream zero-shot performance. XNLI numbers are accuracies. POS numbers are F1 scores. ---- indicate that the language was not present in the subset that mNMT was trained on.} \label{tab:numlang}
\end{table}

\paragraph{Effect of the Target Language Token}
During the pre-training step, when we perform the translation task using the mNMT system, we prepend a $<$2xx$>$ token to the source sentence, where xx indicates the target language. The encoder therefore has always seen a $<$2en$>$ token in front of non-English sentences and variety of different tokens depending on the target language in front of English sentence. However, when fine-tuning on downstream tasks, we do not use this token. We believe this creates a mismatch between the pre-training and fine-tuning steps. To investigate this further, we perform a small scale study where we train an mNMT model on 4 languages to and from English in two different settings: 1) where we prepend the $<$2xx$>$ token, and  2) where we don't prepend the $<$2xx$>$ token but instead encode it separately. The decoder jointly attends over both the source sentence encoder and the $<$2xx$>$ token encoding. The BLEU scores on the translation tasks are comparable using both these approaches. The results on cross-lingual zero-shot transfer in both settings are provided in Table \ref{tab:2xxs}. Removing the $<$2xx$>$ token from the source sentence during mNMT training improves cross-lingual effectiveness on both POS tagging and XNLI task. Training a massively multilingual NMT model that supports translation of 102 languages to and from English without using the $<$2xx$>$ token in the encoder is another direction for future work.

\begin{table}[h]
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}{c|cc|cc}
\toprule
    & \multicolumn{2}{c|}{POS}                           & \multicolumn{2}{c}{XNLI}                          \\
\midrule
    & with \textless{}2xx\textgreater{} & without       & with \textless{}2xx\textgreater{} & without       \\
\midrule
en  & 96.3                              & \textbf{96.4} & 80.1                              & \textbf{80.5} \\
es  & 85.1                              & \textbf{86.6} & 74.2                              & \textbf{74.5} \\
fr  & 86.6                              & \textbf{87.8} & 72.9                              & \textbf{73.7} \\
de  & 90.5                              & \textbf{91.1} & 71.4                              & \textbf{72.9} \\
\midrule
avg & 89.6                              & \textbf{90.5} & 74.6                              & \textbf{75.4} \\
\midrule
\end{tabular}}
\caption{Effect of $<$2xx$>$ token on zero-shot cross-lingual performance. XNLI numbers are accuracies. POS numbers are F1 scores.} \label{tab:2xxs}
\end{table}

\section{Related Work} \label{sec: rel_work}
We briefly review widely used approaches in cross-lingual transfer learning and some of the recent work in learning contextual word representations (CWR).

\paragraph{Multilingual Word Embeddings} For cross-lingual transfer, the most widely studied approach is to use multilingual word embeddings as features in neural network models. Several recent efforts have explored methods that align vector spaces for words in different languages \cite{faruqui2014improving,upadhyay2016cross,ruder2017survey}.

\paragraph{Unsupervised CWR} More recent work has shown that CWRs obtained using unsupervised generative pre-training techniques such as language modeling or cloze task~\cite{taylor1953cloze} have led to state-of-the-art results beyond what was achieved with traditional word type representations on many monolingual NLP tasks \cite{peters2018deep,devlin2018bert,howard2018universal,radford2018improving} such as sentence classification, sequence tagging, and question answering. Subsequently, these contextual methods have been extended to produce multilingual representations by training a single model on text from multiple languages which have proven to be very effective for cross lingual transfer \cite{wu2019beto,mulcaire2019polyglot,piresmultilingual}. \citet{lample2019cross} show that adding a translation language modeling (TLM) objective to mBERT's MLM objective utilizes both monolingual and parallel data to further improve the cross-lingual effectiveness.

\paragraph{Representations from NMT} The encoder from an NMT model has been used as yet another effective way to contextualize word vectors \cite{mccann2017learned}. Additionally, recent progress in NMT has enabled one to train multilingual NMT systems that support translation from multiple source languages into multiple target languages within a single model~\cite{johnson2017google}. Our work is more closely related to two very recent works which explore the encoder from multilingual NMT model for cross-lingual transfer learning \cite{eriguchi2018zero,artetxe2018massively}. While \citet{eriguchi2018zero} also consider multilingual systems, they do so on a much smaller scale, training it on only 2 languages. \citet{artetxe2018massively} uses a large scale model comparable to ours with 93 languages but they constrain the model by pooling encoder representations and therefore only obtain a single vector per sequence. Neither of these approaches have been used on token level sequence tagging tasks. Further, neither concern themselves with the performance of the actual translation task whereas we our mNMT model performs comparable to bilingual baselines in terms of translation quality.

\section{Conclusion and Future Work}
\label{conclusion}
We train a massively multilingual NMT system using parallel data from 103 languages and exploit representations extracted from the encoder for cross-lingual transfer on various classification and sequence tagging tasks spanning over 50 languages. We find that the positive language transfer visible in improved translation quality for low resource languages is also reflected in the cross-lingual transferability of the extracted representations. The gains observed on various tasks over mBERT suggest that the translation objective is competitive with specialized approaches to learn cross-lingual embeddings.

We find that there is a trade off between the number of languages in the multilingual model and efficiency of the learned representations due to the limited capacity. Scaling up the model to include more languages without diminishing transfer learning capability is a direction for future work. Finally, one could also consider integrating mBERT's objective with the translation objective to pre-train the mNMT system.

\bibliographystyle{acl_natbib}

redpajama_set_name: RedPajamaArXiv


text:
Q: Jquery: Closest() div remove not working Jquery closest div remove not working i found same question on stackoverflow but these answered not resolve my problem
closest div remove js fiddle here

A: Here is the solution for you https://jsfiddle.net/woeot0L4/1/
$(document).on('click', '.deleter',function(){
   $(this).parent().remove();
});

Since you are appending the deleter class element dynamically, click will not work. Either you need to bind an event to the class or use document on click.

A: You add a click event to the deleter before it is created, since it is created on click to addproduct, which did not happen when the document is loaded. You will need to create an on event, like this:
$('#dynamicInput').on("click", ".deleter", function() {
    $(this).parent().remove();
});

You also need to make sure you do not duplicate HTML ids. It is an incorrect way of HTML programming. When something is unique, use an id. When it is not unique, use a class.

A: Since you add your elements afterwards to the dom, the click-event is not bound to your .deleter. you have to invoke it every time you create a new element like this: https://jsfiddle.net/woeot0L4/5/

redpajama_set_name: RedPajamaStackExchange


The search is on for 11 people missing after a Chinese cargo vessel sank off the southeastern extremities of Hong Kong early on Monday morning following a collision with a large container ship.
A 47-year-old crew member was rescued by fishing boats nearby and is being treated for minor injuries, according to a police spokeswoman.
The Zhong Xing 2 was apparently sailing from Hebei province with a dozen crew and a cargo of cement. The 300-m container ship MOL Motivator, which is registered in the Marshall Islands, was departing Hong Kong for Yantian in Guangdong at the time of the crash.
Hong Kong is one of the world's busiest ports, and visibility is frequently poor at this time of year because of fog. According to the city's Marine Department, 171 people were injured or killed in shipping accidents around the territory last year, down from 232 in 2012 but up from 78 people in 2010.
In 2008, the Ukrainian tugboat Naftogaz-67 collided with a Chinese cargo vessel, the Yao Hai, in local waters with a total loss of 18 lives.


Computer building is a favorite hobby of mine and maybe something I'll pass along to my kids some day (wife permitting). If you're unfamiliar, it might seem intimidating, but it's actually not too hard to get a handle on. You mostly just have to make sure you get parts that will work with each other.
That's really about it. The case holds the other parts and comes in a range of sizes. The one pictured above is a large size (full-tower). Power supplies come in a range of wattages, typically 500W to 1200W. More power is needed for high-end systems with powerful CPU's, multiple graphics cards etc.
The motherboard, CPU (Central Processing Unit — the "brain" of the computer) and RAM are the parts you want to make sure work together. Start with the CPU you'd like to use and then see which motherboard is compatible. The motherboard will tell you which RAM modules it accepts. 8 to 16GB of RAM is typical today.
Some CPU's come with integrated graphics. You won't be playing sophisticated games with them, but otherwise they get the job done. Alternatively, you can look into dedicated graphics cards.
What you'll do with the machine will determine the parts you buy. It could range from a few hundred to several thousand dollars. If you get into this hobby, you might end up looking into air and liquid cooling products, braided cabling, lighting and other non-necessities. That's when the real fun starts, hehe.
Comment with any specific questions you have. I love getting people into this hobby and am happy to help you figure out your build.


Match Parla vs Alcala results and Live score on footlive.com. Parla - Alcala match for Spain: Tercera Division, Group 7 starts on 10/02/2019 at 10:30 UTC/GMT. With footlive.com you can follow Parla results and Alcala results. Live results, Goal Scorers , Half Time result, Full-Time result, 2nd Half result, h2h, head to head, line ups, goals, red cards, yellow cards, statistics, betting, follow the game with foot live.


text:
10 Benefits of homeownership
Realize Your Vision: A Starter Guide to the Prototyping Process
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What Is The Correct Hanine Pronunciation?
The Middle Eastern and Arabic term hanine is pronounced "han-ee-nuh". It's crucial to know the correct hanine pronunciation of the word correctly because there are diverse manners to convey it. Use the correct spelling and maintain a consistent rhotic for the pronunciation that is the most accurate.
Depending on your location and country, Hanine may not sound the same to you. It is pronounced H-a-n-e-e in the southern United States. The midwestern and northern regions of the nation, however, have a different dialect from H-a-n-e-n.
There are numerous ways to pronounce the name word, which has been used for generations. You will sound more sophisticated if you learn how to pronounce it properly. In this article, we will provide information related to its origin, some tips, and advice.
What are the essentials of hanine pronunciation?
Hanine is a challenging Arabic word to pronounce. However, you can pronounce it correctly with a little practice.
Always pronounce the word with the proper Arabic letters. For instance, the letter "a" is pronounced "aa" in Arabic. Therefore, you should pronounce Hanine as "ha-nee-ye."
Try to concentrate on the final "e" sound of the word Hanine when you hear it spoken. The third sound in pronouncing the word is a little trickier to make than the first two, so make sure you practice it until it comes naturally.
When pronouncing the word, don't forget to give it a little extra emphasis. The "e" sound will become more distinct and clear as a result.
When did it originate?
The Middle Eastern country of the Levant is where the name Hanine first appeared. Although the name's origin is unknown, it could be connected to the Hebrew word "hannah," which means "flowers." Traditionally, girls in Egypt, Jordan, Lebanon, and Palestine have been given this name. Additionally well-liked in Tunisia and Morocco. It is primarily found in Louisiana and Mississippi in the United States.
The name Hanine was first used in writing in the late 1800s. Many people think that the name was first used as a baby name in Ancient Greece. However, there is no proof to back up this assertion. The name using the same word was first used formally in America in 1917.
What is the most common mistake in its pronunciation?
The feminine variants of the words "her," "them," "his," and "she" are frequently pronounced incorrectly in Hanine. When these words are pronounced incorrectly, they can sound awkward and false. You can buy a book or audio course that teaches you how to pronounce these Arabic terms correctly if you wish to perfect hanine pronunciation.
What is a useful tip to pronounce it?
Learning the phonetic alphabet can be beneficial if you have difficulties pronouncing it correctly. To make a word simpler to speak, try emphasizing the last letter of the word and concentrating on a particular sound. Once you have mastered this, try practicing with some other Hani terms. Once you get the hang of it, speaking the word correctly won't take you long.
Use your lowest voice to pronounce the word. You can hear the vowels in this way. Making sure to always pronounce the letter h with a kh when saying the word is crucial. The term "gahina" should also be pronounced with a long syllable.
What are the numerous forms of Hanine?
There are numerous ways to say that, so it's crucial to be aware of them all. The following advice will help you pronounce the word properly:
1. Hanine can be written as han. You could say, "I ate a han," for instance.
2. Hain is the extended form of hanine. You could say, "I ate a lot of hain," for instance.
3. Depending on how much emphasis you want to place on the letter n, you can alternatively say ha-nin or ha-nyn. Saying "I ate a ha-nin" or "I ate a lot of ha-nyn" are two examples.
4. Another choice is to simply say "han" and move on. The sound will be "hahn."
How to pronounce it easily?
It can be useful to break the word up into its constituent syllables if you're having trouble pronouncing the name in English. Focus on emphasizing the final letter of the word because it is the most challenging to pronounce. To learn the phonetic pronunciation of more words in Hanine's language, you can also practice saying them aloud.
There are various ways to pronounce it in English, with "h-a-n-i-n-e" being the most typical one. It's also critical to keep in mind that regional differences in pronunciation exist. The Midwestern part of the United States pronounces the name Hanine with a loud H sound. In the Southern United States, it is pronounced with an e.
Final Advice on Pronouncing Hanine Properly
Here are some helpful hints to assist you if you're having problems pronouncing the word.
1. Start out simple. Learn the phonetic alphabet's letter pronunciations. It will be much simpler to pronounce as a result.
2. Strive for continual improvement. Pronunciation is a skill that can be improved with practice.
3. Don't be reluctant to seek assistance. If you're having trouble pronouncing the word, ask a friend or member of your family for assistance. They might be able to provide you with some valuable advice.
What does hanine mean?
The word can have different meanings in different languages and even contexts. It's usually a flower, but it can also be the first name in many countries.
What is the accurate hanine pronunciation?
There is no accurate way to pronounce it as it depends on which part of the world you belong to. However, the most common pronunciation is han-ee-nuh.
Why is there confusion in its pronunciation?
The confusion is due to the word being common in many languages around the world and they all pronounce it using their own accents.
What other words are used to explain its meaning?
Feminine words like flowery, sweet, gentle, and elegant are used to describe hanine's meaning.
If you know what it means when you use it, hanine pronunciation is not that tough. It also depends on where in the world you are from. As a result, you must understand the context in which the word "hanine" is employed.
The name using the same exact words is feminine and means "flower." H-a-n-i-n-e, hanmi, and hah-nah are all pronouns. It has meanings related to gentleness, sweetness, elegance, beauty, and delicacy.
hanine pronunciation
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The University of Central Arkansas is a state-run institution located in the city of Conway, the seat of Faulkner County, north of Little Rock and is the fourth largest university by enrollment in the U.S. state of Arkansas, and the third largest college system in the state. The school is most respected for its programs in Education, Occupational Therapy, and Physical Therapy. It is also the home of the UCA Honors College as well as four Residential Colleges.
As of fall 2014, UCA has an enrollment of 11,698 students. Enrollment for 2013 was 11,534 and fall 2012 was 11,107. Retention for full-time, first-time undergraduates increased from 69.9 percent from fall 2013 to 72.4 percent from fall 2014. Graduate student enrollment is 1,867, while last year the number was 1,856 and the number of transfer students increased to 775 compared to 618 last year.
UCA Colors – The colors for UCA were decided the first year and according to an article in the November 24, 1908 edition of the Log Cabin Democrat, were said to be purple and silver. President Doyne assigned the task of developing school colors to W.O. Wilson and Ida Waldran in 1908. Wilson was wearing a gray sweater and Waldran was wearing a purple scarf. They chose the colors based upon the color of the clothing they were wearing that day. Both Wilson and Waldran thought that purple and gray complemented each other. Today the official colors for all UCA sports teams are purple and gray.
Main Hall – is the oldest building on campus. This building was completed in 1919 and was built by George Donaghey, the man for whom Donaghey Avenue is named and a former governor of the State of Arkansas. After the building was built it served a dual role as the administration building and as a classroom building. It continued to serve as the administration building until the 1960s. On February 11, 2011, the building was named on the National Register of Historic Places.
World War II Marker – UCA's World War II Memorial was dedicated in October 2003. The memorial contains the names and branch of service of forty-six UCA alumni who were killed during World War II. The memorial is a permanent reminder of those UCA alumni who gave their lives fighting for their country.
Senior Legacy Walk Brick Campaign – The Senior Walk is located in the courtyard in front of the Student Center. Each year, graduates will have the opportunity to purchase bricks as part of their class year. For $100, graduates can purchase a brick that will be inscribed with his/her name or the name of a graduate that a purchaser wants to honor.
The University has 6 distinct colleges offering over 80 majors. These colleges are: The College of Business, the College of Education, the College of Fine Arts and Communications, the College of Health and Behavioral Sciences, the College of Liberal Arts, and the College of Natural Sciences and Mathematics.
The university is famous for its innovative Honors College, which has received national recognition for its excellence. Small classes, distinguished faculty, TAG and URGE grants for students, high ACT and SAT scores, and outstanding medical and graduate school admission rates are hallmarks of the College. The Carnegie Foundation said the UCA Honors College was "One of the most widely imitated programs in the country," and former U.S. Secretary of Education Terrell Bell described the Honors College as being "An Ivy League education at a bargain basement price."
In the 2009 Edition of U.S. News & World Report America's Best Colleges, UCA was ranked 54th in Master's Universities South. UCA was also ranked 23rd in the Top Public Schools in Master's Universities South.
In January 2010, UCA mass communication students launched The Fountain (now The Fountain Magazine), a daily source of news and information. The student-created website is named "The Fountain," after the historic landmark located at the university's campus. The Fountain merged with The Echo, UCA's oldest student media outlet, in January 2014 to combine print and online resources. The Fountain Magazine was created following the merger.
The courtyard outside of Irby Hall.
College of Fine Arts and Communication The College of Fine Arts and Communication offers five fields of study for a degree. Available disciplines include music, public relations, communications, art and writing. Many of the college's degrees enable graduates to work or teach in their chosen profession. The college offers both bachelor and graduate programs.
College of Natural Sciences and Mathematics This college offers eight fields of study in physical and natural sciences as well as six pre-professional programs. Undergraduate programs include those in biology, chemistry, computer science, environmental science, general science, mathematics, physics, and secondary science education. Pre-professional programs include pre-dentistry, pre-optometry, pre-medicine, pre-engineering, pre-pharmacy, and pre-veterinary science. The college offers both bachelor's and master's degrees.
College of Business Business students attending the University of Central Arkansas can pursue an associate, graduate, bachelor or MBA degree through this college. Major fields of study include accounting, economics, finance, information systems, management, and marketing.
College of Health and Behavioral Sciences This college offers bachelor and graduate degrees in health, education, psychology, counseling, military science, nursing, therapy and speech pathology. ROTC students at the University of Central Arkansas attend this college for training as a commissioned officer. Courses are also available in fitness and exercise.
College of Liberal Arts There are six fields of study available at the College of Liberal Arts. The college offers graduate and bachelor's degrees. Available fields of study include English, religious studies and philosophy, world languages, political science, sociology, and history. The College also supports the inter-disciplinary Asian studies, gender studies, Latin American studies, and Southern and Arkansas studies minor programs.
College of Education The College of Education offers University of Central Arkansas students Ph.D., bachelor's and master's degrees programs. These programs provide the skills required to work in teaching, educational administration and curriculum development.
There are currently four residential colleges at UCA, Hughes Residential College (1997), E.D.G.E Residential College at State Hall (1999), STARS Residential College at Short-Denney Hall (2006), and S.T.E.M. Residential College at Arkansas Hall (2010). E.P.I.C. Residential College 4th floor Bear Hall (2012).
The courtyard outside of the Student Center.
The University of Central Arkansas is Arkansas's premiere dramatic school. The program promotes active student involvement in all areas of production including acting, stage management, scenery, costumes, lights, sound, props, and management. The theater program participates annually in the Kennedy Center American College Theatre Festival and has walked away with numerous awards. UCA also holds the annual Arkansas High School Audition Day which is a chance for any High School senior interested in majoring in theater to audition before most of the theater programs in the state of Arkansas. Also, UCA Theatre is accredited by the National Association of Schools of Theatre. This accreditation assures students and parents that the UCA Theatre program meets professional standards of quality in theater education and training as set forth by the Association.
The university's athletic teams are known as the Bears for men's teams and Sugar Bears for women's teams. Central Arkansas participates in NCAA at the Division I (Football Championship Subdivision football) level as a member of the Southland Conference. The university fields 17 varsity sports involving over 400 student-athletes. The athletic program includes seven men's sports: baseball, basketball, cross country, football, golf, soccer, and track and field; and eight women's sports: basketball, cross country, golf, soccer, softball, tennis, track & field, and volleyball.
Approximately 10% of UCA's students are members of one of the 25 Greek organizations hosted by the campus. Greek life was established in 1915.
Notable other RSO's include The National Association for the Advancement of Colored People (N.A.A.C.P), Young Democrats, College Libertarians, College Republicans, Sigma Alpha Lambda Honors Society, Student Council for Exceptional Children, PRISM, Business and Information Technology (BIT), Computer Science Club, Model UN, Campus Dialogue, Pre-Med Society, Pre-Pharmacy Club, The Society of Physics Students (SPS at UCA), RUSH Collegiate Ministries, and a myriad of other religious organizations.
Hiroyuki Nishimura, the founder and a former administrator of the most accessed Japanese message board site 2channel; the current owner of the world's largest English language imageboard 4chan.
On Sunday, October 26, 2008, a shooting took place on the UCA campus shortly after 9:00pm CDT, outside of the Arkansas Hall dormitory. Two students – Ryan Henderson, 18, and Chavares Block, 19 – were fatally shot. A third person, 19-year-old Martrevis Norman, a non-student visiting the campus, was shot in the leg and was treated at the nearby Conway Regional Medical Center.
Four suspects were charged in the shooting: Kawin Brockton, 19, Kelsey Perry, 19, Mario Tony, 20, and Brandon Wade, 20.
↑ Martin, Douglas. "Dr. William Harrison, Defender of Abortion Rights, Dies at 75", The New York Times, September 25, 2010. Accessed September 28, 2010.
This page was last modified on 9 January 2016, at 10:06.


text:
Q: Probability two uniform distribution(0,1) = 2/9 Two numbers are independently and uniformly chosen from the interval (0,1). What is the probability that the sum of the numbers is less than 1 and the product of the numbers is less than 2/9? (Note that both conditions hold simultaneously.)
Given than $n_1\sim\operatorname{uniform}(0,1)$ and $n_2\sim\operatorname{uniform}(0,1)$
$$Z = n_1\cdot n_2 \sim \operatorname{uniform}(0,1)$$
If $X\sim \operatorname U(0,1)$ then
$$P(X < x) = \frac{x-a}{b-a}$$
$$P\left(z < \frac{2}{9}\right) =  \frac{\frac{2}{9}-0}{1-0} = \frac{2}{9}$$
Is this correct or am i making an error somewhere?

A: In other words, what is the area of the region in the square $0\le x\le 1,\,0\le y\le 1$ satisfying $x+y<1,\,xy<\tfrac29$? It's the double integral$$\begin{align}\int_0^1dx\int_0^{\min\left\{1-x,\,\tfrac{2}{9x}\right\}}dy&=\int_0^1dx\min\left\{1-x,\,\tfrac{2}{9x}\right\}\\&=\int_0^{1/3}(1-x)dx+\int_{1/3}^{2/3}\tfrac{2}{9x}dx+\int_{2/3}^1(1-x)dx\\&=\tfrac13+\tfrac29\ln2\\&\approx0.4874.\end{align}$$You'll want to double-check my arithmetic, but Monte Carlo agrees.


Operating a company in the supply chain and logistics disciplines means maintaining a carefully constructed balance of departments, operations and materials to make sure your products or services are available to your clients or customers in a timely, satisfactory manner that compromises neither efficiency nor quality. Focused on the ultimate goal of supplying the finished product to the end user, supply chains must coordinate and communicate between multiple departments, disciplines and functions — including planning, procurement, inventory, manufacturing, production, warehousing, transportation, distribution and customer service.
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Directors and Senior Managers: Responsible for organizing, managing, directing and evaluating budgets and operations in warehousing, transportation, materials handling, inventory control and distribution in the supply chain.
Presidents and Vice Presidents: Leadership, Knowledge, Experience, Creativity and Execution. These are the traits and capabilities you want in a CLO, VP of Logistics or Supply Chain, or Director of Logistics or Supply Chain. At this senior level, the Chief Logistics Executive oversees an array of operations, activities, assets and resources. These can include global and domestic supply chain management, logistics, warehousing and distribution, freight transportation, supply chain planning, logistics and other outsourced services, information technology, procurement, sourcing, materials management, inventory control, demand planning, forecasting, customer service, import/export/compliance, production planning, industrial engineering and more.
People with these traits aren't easy to find. That's where OptimumSCR comes in. OptimumSCR has the connections, tools, and skills and know-how to find the right senior logistics executive for your company's need. Our reach extends across all industries. Chief Logistics Officer is just one example of the type of the high-level positions we at Optimum Supply Chain Recruiters help our clients fill. To speak with one of our logistics headhunters, contact OptimumSCR. Our Interim Division can also help you attain your specific goals by hiring an in-house consultant.
Retained Search: Our retained search services are typically needed when a company has a director level and above position open. When this occurs, we work directly with your team following a 10-step process to successfully fill your open position. Unlike other large search firms, we also offer a 12-month unconditional guarantee period on all of our retained services. Learn more about what makes our retained search services today!
Dedicated Engagement: Explicitly targeted toward high-level and executive positions, our dedicated engagement recruitment service is a retained search hybrid where you receive personalized attention from a dedicated recruiter, who will work to communicate and coordinate with your team for the best understanding of your company goals and the position in question. From our initial discovery and collaboration phase to providing you with weekly progress reports of prospective candidates, we're deeply invested in finding the right talent to fill your vacancy. We even interview, assess and qualify the top candidates for you, offering you an evaluation for your hiring purposes and saving you so much time and trouble.
Contingency Recruiting: Focused on standard and lower-level positions, our contingency recruiting services work directly with your company to understand its goals and fill your vacancy with the candidate most qualified and fitting for your company. Our process involves developing a position profile and description, working on a prospect research strategy, identifying potential candidates to present to you and assisting you with every step of interviewing, assessing references and offering the position. We follow through with our placement by following up, and we're with you through the entire process — plus, you won't pay until your candidate is successfully placed.
As a top logistics recruitment agency, OptimumSCR understands your needs exactly, and we will work tirelessly to provide you with the motivated candidates your company needs. Contact us today to learn more about how we can help you.