Now their lease on the original 42nd street location has come up, and they have decided not to renew as it's just not big enough to fill their needs. Instead, they are relocating it to 555 NE Couch St. at 6th.
The new space which is being designed by Accelerated Development's Keith Shrader who also designed the Nicolai store, will have more room for education, a much larger seating area, and they hope to eventually server beer and wine. The Couch store will showcase the first Steampunk brewing system in the Northwest.
Right before I left for the Specialty Coffee Association of America show in Boston (waaaay back in early April), I stopped in Olympia to chat with Olympia Coffee Roasters co-owner and total sweetheart Oliver Stormshak. (Oliver and I presented a session at the SCAA together about the power of storytelling.) The roastery was abuzz with activity as the crew prepared to open their third Olympia location, which is set to become the company's flagship location. It's centrally located in the gorgeous 1938 Wildwood Building. The new cafe is STUNNING with hand-made tiles imported from Nicaragua, delicious art-deco-y interior arches, and maple accents. It's modern and classic, and does perfect justice to the coffee. Over on Sprudge, you can find some photos that show off the space.
Recently, Heart owner Wille Yli Luoma (say that three times fast) hosted me for an impromptu water tasting. Heart is installing a water filtration system that will allow them to finely tune the hardness of the water, which has a sizable impact on flavor (believe me — we tasted the same coffee with two different "recipes" of water and the flavor difference was shocking). In any case, Wille let slip that Heart was signing a lease later that day on a new downtown spot. This is perfect for Heart. Their east Burnside location in SE Portland has been great for them, but the new spot is tucked in among a number of design-savvy businesses that echo Heart's minimalist, cosmopolitan vibe—home furnishings meccas Canoe and Alder and Co., cocktail lounge Kask, and Alpine-influenced restaurant Grüner (and, one of my favorite totally-out-of-reach boutiques, The English Department). It's also just a hop, skip, and jump from another recent addition to the neighborhood, Blue Star Donuts. (Basil blueberry glaze anyone? Valhrona chocolate? Mmmm.) It's perfect—and will make for some very enjoyable weekend mornings, sipping Americanos while I drool over brass kitchen fixtures at Canoe.
Water Avenue Coffee, located in Portland's inner eastside industrial district, is partnering with Enso Urban Winery to offer a full coffee menu inside the winery's tasting lounge. Enso has led the way in Portland's burgeoning urban winery scene. The new cafe concept will include coffee-wine pairing options and collaborative tastings, exploring the intersection of wine and coffee palates. And though you don't need any more reason than that to love the idea, there are also these awesome bags of sangria. The coffee cart will operate in the morning and early afternoon (current hours: 7 am to 1 pm), and wine tastings will start at the very respectable hour of 4 on weekdays, 2 on weekends.
Equator, which has been roasting in the Bay Area for over 15 years, is opening its first retail location between Mill Valley and Marin, just north of San Francisco. The coffee bar is inside the Proof Lab Surf Shop, a community-oriented surf-and-skate shop. Proof Lab has incubated a number of small businesses (including an art studio for toddlers, a landscaping design firm, and a ceramics studio). Proof Labs and Equator both have longstanding sustainability missions. Mix that with Equator's award-winning Geisha, the Marin sunshine, and the "vintage surf" decor and you have a perfect excuse for a daytrip north.
…so keep your fingers crossed.
When I interviewed Jason Benford of De La Paz Coffee Roasters in San Francisco for Left Coast Roast, we talked among saw horses. At the time—way back in early 2011—the company was planning to open a new cafe modeled on a nieghborhood bar—lots of counter stools, no obvious place to stand in line, lots of wood. As the book went to the print, we ended up pulling out a few sentences that described the space. De La Paz was running way behind schedule and it hadn't opened yet. (It's never good form to write about something that hasn't happened yet in a book, our most permanent of literary forms—well, after petroglyphs).
Four Barrel owners Jeremy Tooker, Jodi Geren, and Tal Mor…are planning to build out a new cafe in the company's Mission St. roastery. Geren spoke to us about the Four Barrel group's plans for their newest acquisition: "We want [De La Paz] to be a side project that's different from Four Barrel, and focused on a different kind of coffee. While Four Barrel is focused on single-origin coffees, De La Paz will be focused on blends…we're really excited to do this other kind of coffee, that focuses on sweetness and body." … The Four Barrel team's next task is building out a cafe in the De La Paz roastery, a project Benford had already embarked upon with Seth Boor of Boor Bridges Architecture, but had yet to complete.
So, another exciting project from Four Barrel, which is just on a roll these days.
Stumptown is officially a player. Hot on the heels of a major venture capital investment in 2011, the company has finally moved out of its scattershot, undersized digs and into a rehabbed, 37,000-square-foot space that will house its roastery and administrative offices (these were formerly spread over three buildings, two of them houses in a SE Portland neighborhood). With a new 90-kilo Probat, and a major investment in top-notch sustainable technology, Stumptown is off to the races. The new facility, whose construction reportedly contributed 200 jobs to a depressed local market, was feted by the major of Portland as well as U.S. Rep Earl Blumenauer in a ribbon-cutting ceremony yesterday.
What started as a tiny microroastery in 1999 is hitting the (sorta) big time. With 2 million pounds of coffee a year and upwards of $35 million in revenue, Stumptown sits firmly among the ranks of other mid-sized regional roasters like Batdorf and Bronson and Caffe Vita.
The new space is gorgeous, full of refurbished old wood, with floor-to-ceiling windows everywhere, including looking into the roastery from the street.
Below are some pictures from a party celebrating the new space took place last night with Stumptown employees and friends.
The new building boasts reclaimed wood and windows everywhere.
When Stumptown's first location on Division Street opened, it was in a former hair salon. The company's signature espresso blend, Hair Bender, was named after the salon. Now, the original sign hangs in the roastery, next to a floor-to-ceiling window into the cupping room.
Stumptown's main upgrade in the new facility is a 90-kilo Probat roaster from the 1960s. But they will also have the original 5 kilo Probat on which Sorenson founded the company in 1999 (it's the oldest working production Probat in the U.S., built in 1917), as well as a 15-kilo roaster and 60-kilo roaster (currently being refurbished).
It's always loud at Stumptown, whether it's from roasting coffee or throwing a huge party to celebrate a new era for the company.
The new location has 37,000 square feet of production and office space, all of it handsomely outfitted in refurbished original or reclaimed wood.
From the cupping lab, a row of antique spittoons for coffee tasters to spit in.
Always too cool for school. A roaster's motorcycle is parked just outside the roastery door.
On a recent trip to Denmark to visit family, I was fortunate to get to visit one of the contenders for best boutique roastery in the world: Coffee Collective.
Years ago, I met the Collective's founder (Klaus Thomsen, then of Estate Coffee) in Portland. After winning the World Barista Champion title he was hosting a local competition at which I was judging. I'm not sure he could have been any nicer. Regardless, he went on to found and foster one of the most highly regarded coffee companies in the world.
What's so great about Coffee Collective? For one, the fluffy stuff: The design geek part of me is instantly enamored of their look. The whole colorful geometry thing may have jumped the shark a bit (especially in Europe — jeez, it's everywhere) but that doesn't stop me from loving it.
And I adore any high end coffee company that serves a shakerato (espresso with simple syrup, shaken in a cocktail shaker with ice). Something magical happens in the shaker that brightens the acidity to a crisp, refreshing peak. It's proof that not all sugar is anathema to good coffee. It's summery and fun.
In Portland, Heart Roasters has worked to bring the Nordic approach to the West Coast, and I'm thrilled to have been able to visit one of their muses.
I'll say one critical thing, though—not about the Collective specifically, but about high end coffee more generally. There's an aspect of it that does exactly what I think it's proponents have tried to avoid: It repeats itself. Coffee Collective, while gorgeous, is not terribly different looking or feeling than other high end coffee bars in Europe and the U.S. (This isn't necesarily because they have intentionally mimicked others—perhaps the reality is more the other way around.) In any case, it's a place I feel instantly comfortable because it speaks the same visual language as other high end coffee bars. I know exactly where I am the minute I step inside one of these spaces. Don't get me wrong — I love 'em. But there's a sameness to them that I think goes unquestioned. And it goes deeper than just how things look: I was somewhat surprised to find on their small menu a coffee from the Finca Vista Hermosa farm in Guatemala. The farm is owned by Edwin Martinez, who spends the better part of each year in Bellingham, Washington. Partly as a result, his direct trade coffees are almost ubiquitous in high end cafes in the Northwest. It's a bit of a shock to go all the way to Denmark and find the same coffees as I would in Portland. But the apex of the coffee market is a small place. Which also makes it a crowded one—and occasionally an echo chamber. This is especially tricky for roasters that focus on single origin coffees. Without a house blend to define them, these roasters become in some ways an extension of the farm brands they carry (and let's not mince words, farms like Vista Hermosa have indeed become brands). That makes it doubly difficult to differentiate in substantive ways from other high end roasters that carry the same coffees. It's something I've been mulling for a while—and running into a familiar coffee in Denmark just brought it all home.
With three different siphon pots, all-glass and wood-handled chemexes, an aeropress, porcelain V60s with your choice of regular paper filters or a Coava Able cone [ed.], a clever coffee dripper, french presses and press pots, and I'm pretty sure I saw a woodneck around here at one point in time.
The cafe was chosen as Best Cup of Coffee, Best Coffeehouse, and Best Place to Meet Someone by the readership of one of Tacoma's alt-weeklies, The Weekly Volcano—not too shabby for a year-old operation. I'll be in Tacoma in October or November for a book event, and I'm looking forward to stopping by.
I recently spent a morning visiting with Augusto Carvalho Dias and Rob Hoos, the owner and roaster (respectively) of Nossa Familia Coffee ("Our Family" in Portuguese). Nossa is a somewhat unusual company in that the coffee they roast comes directly from farms in Brazil owned by various members of Augusto's family—they call it Family Traded.
Though they have been roasting for more than five years in Portland, the company has kept a mostly low profile, selling primarily to offices and restaurants. But recently, they've expanded to a new space in the Pearl District, upped the ante on roasting with a new, energy efficient Loring Smart Roaster (the only one in Portland), and are considering plans for a cafe.
In many ways Nossa Familia is a roasting company that is doing something qualitatively different than other roasters in Portland—with the family traded model, with the Loring, and otherwise. But without a showcase cafe, they've lacked the "street cred" of other roasting companies. (It's a funny thing about coffee—it's hard to be a well regarded coffee company without a cafe.) Their choices and business model differ from the norm—which is a good thing. (My mantra: Variety, especially in coffee, is almost never bad.) Because they operate a bit differently, people don't always know what to make of Nossa. For example, the family trade model has both some significant upsides—deep, direct connections to the stories behind each coffee, open lines of communication for feedback and improvement, the ability to focus on one origin, among others. It also had a few possible downsides—sourcing from a small number of farms in a single country limits variety, and poses potential difficulties with keeping coffee fresh.
How do you guys handle "seasonality"?
We handle seasonality by making sure that we are only purchasing the freshest available crop from our farms in Brazil at the time. My family knows the quality that we like and expect here at Nossa Familia Coffee. We cup the coffees before each shipment to approve the samples. The harvest cycle in Brazil is long starting in May and ending in September. After the harvest the coffees have to rest for at least 60 days, then they are sorted for screen size, density, and all defects removed. Once this is done they wait for shipment in a climate (humidity) controlled environment. My family is working with a university in Brazil to test the difference between burlap, grainpro and a new material. So this coming year all coffees that are further out from harvest will likely be in grain-pro or the new material they are developing.
Side note: Augusto led a side-by-side cupping of the same microlot of coffee, one stored in GrainPro, and one stored in burlap then transferred to GrainPro for shipping. The different in taste was remarkable. I actually quite liked both (they were only three months out from harvest, so still both quite fresh in the scheme of things), but it was amazing how toned up or down certain flavors were.
Coming from a single origin, does that present a problem?
I see it as an opportunity. All coffees change throughout the year, and instead of ignoring that we work with the coffee to adjust our roast profiles. The humidity controlled environment in Brazil is great, and we have never had any customer issues or drop in quality as the year progresses. Grainpro bags are also a huge help.
It is our goal to have the most optimum Brazilian green that we can get our hands on. It is important to us that acidity and organic volatiles are present at peak levels for us to achieve the best roasted results. With each container, we are purchasing more and more coffee in grainpro packaging to increase green quality in order to move more in this direction.
You hear a lot about strip picking in Brazil—often with a sneering tone. I don't know coffee harvesting from Adam, but I wonder if you could provide any info on the harvesting practices at Nossa family farms.
This is a subject that we could talk about for 2 hours over coffee, or beer. I used to be a total detractor of strip-picking. It just sounds bad and "unromantic" compared to hand-picking. That was until I visited Brazil during a recent November and saw a farm where a portion of coffee cherries had not been picked at all and were rotting on the plants. A combination of higher costs and a shortage in labor meant that good cherry went unpicked. I started learning more about the economics of coffee farming and the amount that most farms in Brazil have invested in post-harvest selection. The mills at the farms do an amazing job of separating ripe, green, dried, and over-ripe cherries. The result is that a farm will have some excellent lots and some sub-par lots. That's okay—there are roasters buying the sub-par lots. We happen to buy the excellent lots. The total percentage of excellent coffee in a farm is reduced compared with cherry picking, but the high end lots are just as high end and it allows the farm to stay in business.
What are the farms that Nossa imports for/buys from/roasts?
Do you have a sense of how large or small your purchases are compared with total sales for this farm (are you a relatively small buyer, or a big one for them)?
A majority of our beans come from Fazenda Cachoeira da Grama. Cachoeira produces up to 5,000 bags annually of which, we purchase approximately 1,200. We also purchase organic farms through my cousin's export company, they help us source organic beans from a few local certified farms.
In Left Coast Roast, I called Nectar Coffee Company "One to Watch." Sadly, the only place to watch the wee company today is in the rearview mirror. The short-lived but much-loved operation was the effort of veteran roaster Todd Weiler, who roasted on a tiny Ambex YM-2. For nearly two years, he sold his whole bean single origin coffee along with steamy pour overs at local farmer's markets in the Portland suburbs. Weiler made his way to Portland via roasting stints for both Flying Goat and Intelligentsia in California.
The Blue Bottle roasting works. Photo courtesy of @toddweiler1.
Now, he has headed back to California to take a pretty wonderful job as lead roaster at Blue Bottle Coffee in Oakland. You can keep track of his exploits on Twitter at @toddweiler1.


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Will Lufthansa weaken on TAP Air Portugal?
FRANKFURT - Portugal is preparing to reprivatize TAP. Lufthansa falls into the grid of potential buyers. In fact, according to circles,...
Luis Enrique makes the list for the duels of Switzerland and Portugal with doubts above and behind this Friday
MADRID, 15 September (EUROPA PRESS) - The men's national soccer coach,...
TAP Air Portugal is back on display
LISBON - TAP Air Portugal has gone from public to private twice - and back again. According to media reports, the government...
Iberdrola commissions its first photovoltaic system in Portugal, which will power 11,000 homes
Aisa john - August 16, 2022
MADRID, August 16 (EUROPA PRESS) - Iberdrola has launched the Algeruz...
Scholz advocates increased gas imports from Portugal and Spain
BERLIN, August 11 (DPA/EP) - Chancellor Olaf Scholz has announced he...
Hot summer: forest fires in France and Portugal
Aisa john - August 1, 2022
Iberdrola and BP will invest 1,000 million in 11,000 charging points for electric cars in Spain and Portugal
Spanish power group Iberdrola and British oil company BP this Thursday announced strategic alliances to invest €1,000 million to build 11,000 charging points...
Rui Moreira, Mayor of Porto: "Portugal should have regions with political power like Spain"
In recent years, Portugal has embarked on a timid process of decentralizing powers to the municipalities, which this legislature should consolidate and strengthen...
The WHO warns that more than 1,700 people have already died from the heat wave this year in Spain and Portugal alone
MADRID, July 22 (EUROPA PRESS) - The Director of the Regional...

redpajama_set_name: RedPajamaCommonCrawl


text:
Your MACREX indexing software search returned 10 matches.
Helmut Filacchione, PhD
Complete Indexing
Waynesboro, Virginia United States
I write back-of-book and embedded indexes in the areas of the sciences and humanities. Specifically: sciences, physics, engineering, technology, energy, environment, business, economics, finance, and management, as well as cognitive Details...
Jessica Hinds-Bond, PhD
Good Strong Words
Chicago, Illinois United States
I am an indexer and editor who brings to my work a keen eye for detail and a passion for supporting scholarly writing. As an indexer, I specialize in scholarly Details...
Pierke Bosschieter
ISB&index
Stitswerd, Netherlands
Indexer with more than 15 years' experience in Indexing academic books in the humanities. Specialized in Middle Eastern & Islamic studies, Jewish and Holocaust studies and Religion & Philosophy. Delivers Details...
Marilyn Anderson, PhD
AndersonIndex
Fort Collins, Colorado USA
Experienced full-time indexer. Wide range of books and materials indexed. Specialty in scientific, medical, and environmental materials. PhD in Biological Sciences. Additional subject fields include arts, business, culture, current events, Details...
Cynthia Col, PhD
Cynthia Col
Watertown, Massachusetts USA
I specialize in scholarly material in the humanities and social sciences. I hold an interdisciplinary Ph.D. that concerns China, Tibet, art history, architecture, and material culture. I am particularly interested Details...
Molly Hall
Over 25 years creating precise and concise indexes with particular expertise in the fields of behavior sciences, environmental/natural sciences, sustainable living, education, gender studies, life sciences, social/cultural history and theory, Details...
Janet Perlman
Southwest Indexing
Phoenix, Arizona USA
Indexing since 1961. Full-time indexer with BS in chemistry, MA in management. Indexing of English and Spanish materials. Past projects include textbooks, reference books, and trade books. Specialty in hard Details...
Helene Ferranti
Cloudberry Indexing
Charlestown, Massachusetts USA
Indexer of scholarly, trade, and text books with special interest and expertise in history and biography, environmental studies, sailing, film and performing arts, literary criticism. Degrees in History (BA – Details...


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text:
About Exmouth Journal
Sidmouth Herald
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Lympstone life has been shattered
PUBLISHED: 01:01 13 March 2008 | UPDATED: 08:53 10 June 2010
Last Saturday, Lympstone was encouraged to celebrate the official opening of the cycle path from Exmouth. I, however, was in mourning mourning the loss of a village that I grew up in. Its charm has been destroyed with the arrival of the path and hordes
Last Saturday, Lympstone was encouraged to celebrate the official opening of the cycle path from Exmouth.I, however, was in mourning - mourning the loss of a village that I grew up in. Its charm has been destroyed with the arrival of the path and hordes of path users. By routing vast numbers of walkers and cyclists straight through the heart of the village, it has lost the unique sense of community and tranquillity that have made it such a delightful village to live in. Visitors have always been welcome, but their numbers have never before impacted on the village adversely. In their choice of route, Devon County Council has destroyed this quiet backwater for everyone and turned it into a through route. Driving and cycling through feels unsafe now and living here feels different - like the village is some quaint exhibit on show to thousands.Mr Reece, Councillor Peter Acca and others have vocalised in their letters what lots of Lympstone residents have been thinking - why does the trail leave the railway line after only one and a half miles and make a dangerous diversion along our one of our village's narrowest lanes? Did the person at County Hall drawing the line on the map really think about the safety of users or the impact on our community, or is this a cheap short cut?I am appalled by Peter Acca's revelation that Devon County Council has ignored our parish council's advice and been deliberately obstructive when asked for information which affects the safety of trail users and the lives of the villagers. DCC has withheld risk assessment information that our parish council has requested and ignored approaches by our concerned police officer. I'm disappointed that more people did not get behind our local council's campaign for a safer route - perhaps because they were unaware of their efforts. There was nothing about it in our parish newsletter and I feel the whole thing was hushed up.I voiced concerns myself at the DCC exhibition set up in the village to promote the trail. When I asked about projected number,s I was told DCC couldn't really say, but that the council didn't anticipate user numbers would be large enough to cause traffic problems.... SPIN! It's chaos at the weekends already and that's without tourists thrown in and users coming at us from both directions. If anything like The Camel Trail's 400,000 people use the trail annually, we will be gridlocked and feel like we are under siege.The trail should stay alongside the railway track to Underhill car park, as the parish council suggested, so avoiding the hazards of Sowden Lane and Cox's Hill. Those wishing to explore the village and enjoy its pubs, shops etc could leave their bikes where there is plenty of parking and public toilets (the only ones between Exmouth and Topsham). It's an obvious choice of route when safety is really given priority, and functionality is considered. DCC has a responsibility to both villagers and users to create a proper 'trail', not just a number of short off-road sections linked by dangerous roads. (Exton and Topsham will be adversely affected, too, by large numbers of users or poor roads through residential areas.) With so many potential accident black spots along the route, how can DCC brand this 'The Exe Estuary Trail'? It's misleading. 'Assault Course' might be more appropriate - not for the faint-hearted and not designed with young children in mind.J Evans, The Strand, Lympstone.
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As the warm and sunny weather has arrived at your country, your friend decides to visit his cottage located in the countryside because he would like to rest there and get refreshed. What is more, he invites you to the building too and as you have some spare time, you make up your mind and start off to the location of the edifice. However, at the time you arrive at the given address, you come to know that the house is in the middle of the lake and you need to find a way to get into it! Take out your smart ideas to solve this situation in a new Wow Escape pastime, the Deep Lake House Escape! Keep it up!


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35 Inspirational Russell Simmons Quotes On Success
Russell Simmons is an American entrepreneur, producer, and author. The Chairman and CEO of Rush Communications, he cofounded the hip-hop music label Def Jam Recordings and created the clothing fashion lines Phat Farm, Argyleculture, and Tantris. May his quotes inspire you to stay focused on your dreams and live life on your own terms.
1. "In my experience, there's only one thing that will always steer you toward success: That's to have a vision and to stick with it. Once I have a vision for a new venture, I'm going to ride that vision until the wheels come off." Russell Simmons
2. "The person who stays focused on his dream, realizes his dream." Russell Simmons
3. "Surround yourself with people who are smarter than you. Pick people who are interested in what you're interested in." Russell Simmons
4. "I don't do anything until I meditate." Russell Simmons
5. "If you give a good thing to the world, then over time your karma will be good, and you'll receive good." Russell Simmons
6. "I won't make a deal that's only favorable to me." Russell Simmons
7. "If you wake up deciding what you want to give versus what you're going to get, you become a more successful person." Russell Simmons
8. "Be encouraged. Stay on your hustle. You can't fail until you quit." Russell Simmons
9. "Stop running to those who ignore you and start running to those who adore you." Russell Simmons
10. "You just have to pay attention to what people need and what has not been done." Russell Simmons
11. "I've been blessed to find people who are smarter than I am, and they help me to execute the vision I have." Russell Simmons
12. "Keep going until you break through that wall." Russell Simmons
13. "Needing nothing attracts everything." Russell Simmons
14. "Money doesn't make you happy, but happy makes you money." Russell Simmons
15. "Don't get caught up in the hype. Always be connected to and content with, what's happening in your own head." Russell Simmons
16. "Walk out into the world with a bounce in your step and a song in your heart." Russell Simmons
17. "Success is happiness." Russell Simmons
18. "Everyone needs an opportunity to discover, or rediscover, what God gave them." Russell Simmons
19. "Art allows people a way to dream their way out of their struggle." Russell Simmons
20. "Compassion is the ultimate expression of your highest self." Russell Simmons
21. "I look for people who have insight in places where I don't." Russell Simmons
22. "I want to fight poverty and ignorance." Russell Simmons
23. "Every day set the simple goal of being more awake and less distracted." Russell Simmons
24. "It's all about the work. Nothing is going to fall into your lap. Live your life as if you already are where you want to be." Russell Simmons
25. "I think I'm less afraid of failure than some others." Russell Simmons
26. "Don't ruin a good today by thinking of a bad yesterday. Let it go." Russell Simmons
27. "Service is the key to everything in life." Russell Simmons
28. "I don't miss my prayers and I don't miss my yoga. Those things are important to me." Russell Simmons
29. "My financial service business is the absolute best thing I have done to empower the poor. This company is the greatest gift I've given." Russell Simmons
30. "Find peace within yourself." Russell Simmons
31. "Always focus on your effort, instead of the results of that effort." Russell Simmons
32. "The key ingredient to any kind of happiness or success is to never give less than your best." Russell Simmons
33. "What we want people to do is operate from the inside out instead of the outside in." Russell Simmons
34. "Gratefulness is a practice, just like happiness is a choice." Russell Simmons
35. "As I get, I give. Giving as you get is critical. It has everything to do with being happy for yourself, and making others happy is the cause of making yourself happy, and it's the cycle of giving and getting." Russell Simmons
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Attila Dragóner (born 15 November 1974 in Budapest) is a Hungarian former footballer.

He was a defender for Ferencvárosi Torna Club in Budapest, Hungary. Dragóner was a key player in defence and counterattacking. He played for Ferencvárosi TC, in his native Hungary. He also played for the Hungarian national football team.

Honours
Ferencvárosi TC
 Nemzeti Bajnokság I: 2001, 2004; Runners-up: 1998, 2002, 2003
 Magyar Kupa: 2003, 2004; Runner-up: 2005
 Szuperkupa: 2004

References

External links
 HLSZ profile

1974 births
Living people
Footballers from Budapest
Hungarian footballers
Hungary international footballers
Footballers at the 1996 Summer Olympics
Olympic footballers of Hungary
Hungarian expatriate footballers
Veszprém LC footballers
Stadler FC footballers
Budapesti VSC footballers
Ferencvárosi TC footballers
SC Fortuna Köln players
Vitória S.C. players
2. Bundesliga players
Expatriate footballers in Germany
Hungarian expatriate sportspeople in Germany
Expatriate footballers in Portugal
Hungarian expatriate sportspeople in Portugal
Association football defenders

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    }
}
window.QForms.RowFormDateFieldController = RowFormDateFieldController;

redpajama_set_name: RedPajamaGithub


text:
End the Tri Season in Style
Don't hit the couch just yet - get your head down, keep racing and end the season on a high
By Michelle Arthurs
The season is coming to an end, so you'd be forgiven for looking forward to a bit of a rest. But not yet. There is still time left to go that extra mile and squeeze in some fantastic results to keep you inspired and motivated throughout winter.
Age-group world champion (30-34) Jill Parker says, "Winter training is tough, so while you are super-fit from all you've been doing over summer, it is best to give yourself a kick now and reap the rewards later."
However, it isn't always easy to maintain motivation at this time of the year, according to two-time Ironman 70.3 champion Emma-Kate Lidbury (eklidbury.co.uk). "People often overlook the mental aspect of training and racing. It can become very difficult to keep going, especially at the very end of the season," she says.
Cop an attitude
To avoid a slump, a positive attitude could be your best weapon. "As the season nears its end I encourage athletes to reflect on the success they've had," says Steve Lloyd, head coach at Absolute Tri (absolutetri.com).
"Remember the goals and targets you set before the season started. If your motivation starts to fade, remind yourself how many of those goals you've checked off. It's easy to become fixated on the times you're achieving when you're desperate to knock a few seconds off. You can forget how much you've improved since the start of your journey with triathlon."
Fill up your racing diary
Lloyd suggests that triathletes enter a few local races at the end of the season. Because you're more relaxed at this stage of the year, and familiar with the course, you may perform better than you expect.
Mix up the training
A little training reshuffle could also help maintain your interest and commitment at the end of the season.
Coach Pete Freedman (driventotri.com) explains: "There is no point cramming in training late in the season; you're better to rely on the base you have built up over the previous winter. Slightly reduce the mileage and increase the intensity as the end of the season approaches.
"You could try some different types of sessions, partly to freshen up the training but also to try out some training methods for the following season."
It's easy to become complacent (and lazy) with the bulk of racing out of the way, but now is the time to draw upon your hard work and cross that final line in style.
Get ready to relax
One method of maintaining momentum is to envisage the rest period to come. Joanna Carritt (joannacarritt.co.uk), pro triathlete and member of The Challenger World UK tri team for 2011, says, "When it gets tough during your preparations, you need to know that you're going to get some time off. Plan what you'll do with all that time so that you have something to look forward to."
Rediscover the fun
You can also use the final races to re-engage with the fun side of the sport. "There should always be a place in the calendar for 'fun' races. After all, 99 per cent of us do not earn a living from the sport; it's a hobby. If it's not fun, then why do it?" says Ironman coach Simon Ward (thetriathloncoach.com).
Parker, who coaches part-time, agrees: "When faced with the end of the season I tend to add in a race for pure enjoyment. Last year I did the Hever Castle Triathlon [in Kent] with my husband and some friends, the majority of whom had not done a triathlon before. It was great to let friends into my weird 'tri world'."
Lidbury has a similar approach: "When I raced as an age grouper, the end of the season was always about a few fun events or doing something a bit different. It was a great way to stay motivated and keep things fun and fresh."
...And relax
Once the races have truly petered out, there will be time to relax and unwind before you plan your next steps.
"I always use the end of the season to make time to see many of the friends I end up neglecting when I'm busy racing and training. I make a conscious effort to switch off from triathlon," says Lidbury.
Parker did much the same when she was a pro: "I always had a month off, to cram in seeing friends and family I had neglected during the season. I'd party hard and get it out of my system for the next 11 months."
Carritt, who coaches with Everyday Training (everydaytraining.org.uk), says, "If you race Ironman, then there really isn't a natural 'season' - you can race all year round if you are prepared to travel. The last few years I have raced all year without really having a proper 'end of season'. This is not something I'd recommend for age groupers. It's important to plan your breaks as well as your training."
Take a proper break
Carritt suggests a break of at least a month from structured training. "You should keep ticking over with club sessions and rides with friends that you enjoy, but I feel that you need a mental break at least as much as physical rest."
It can be hard for triathletes to truly switch off when the seasons ends. "Age-group triathletes generally are very obsessive; they tend to be successful, highly motivated individuals, which means they struggle to take time off," says Freedman.
The key is to consider the positive aspects of rest and recovery: your body will bounce back, adapt to the training you've done during the year and you will not develop the contempt that can come with familiarity. So, don't stop just yet, but do stop, for a while. There's always next season.
Check if your kit is in good enough nick for 2012.
More From Triathlon
Ironman Weymouth 70.3: Race Report
11 best wetsuits
How to train for an Ironman 70.3
Improve your swim stroke with these expert tips
How do I get used to running off my bike in triathlons?
Training for a race? How to take your training on holiday
How should I train for my first Olympic-distance triathlon?
Q&A: Gordon Benson
Q&A: Alice Hector
Essential guide to open-water swimming


text:
Q: Error while downloading file using ASP.net c# Below is code where AJAX send POST request
Basically I want to download a file from some location(full path) on page load
But when I this page is called no error is thrown page get call succesfully
Even I get file.Exist=true , Response is created but on browser nothing happens
I expecting file download dialog. on further debugging I found exception in Response object
Headers = 'Response.Headers' threw an exception of type 'System.PlatformNotSupportedException'
This operation requires IIS integrated pipeline mode
I am running this web page in my localhost(visual studio development server). .net4 and VS2010
On Page Load Code:
  protected void Page_Load(object sender, EventArgs e)
    {

        string path = Request.Form["path"];

       // string reportName=path.Substring( path.LastIndexOf("\\") ,path.Length);
        DAL.IO_helper i = new DAL.IO_helper();
        string fullpath = i.GetReportsPath() + path;

        FileInfo report = new FileInfo(fullpath);

        if (report.Exists)
        {

            Response.ClearContent();

            Response.ClearHeaders();
            // Add the file name and attachment, which will force the open/cancel/save dialog box to show, to the header
            Response.AddHeader("Content-Disposition", "attachment; filename=" + report.Name);

            // Add the file size into the response header
            Response.AddHeader("Content-Length", report.Length.ToString());

            // Set the ContentType
            Response.ContentType = ReturnExtension(report.Extension.ToLower());

            // Write the file into the response (TransmitFile is for ASP.NET 2.0. In ASP.NET 1.1 you have to use WriteFile instead)
            Response.TransmitFile(report.FullName);

            //Transmit file

            Response.Flush();

            // End the response
            Response.End();

        }
        else
        {
            Response.ClearContent();
            // End the response
            Response.End();
        }

    }

function
 private string ReturnExtension(string fileExtension)
        {
            switch (fileExtension)
            {

                case ".txt":
                    return "text/plain";
                case ".dat":
                    return "text/plain";
                case ".doc":
                    return "application/ms-word";
                case ".docx":
                    return "application/vnd.openxmlformats-officedocument.wordprocessingml.document";
                case ".zip":
                    return "application/zip";
                case ".xls":
                    return "application/vnd.ms-excel";
                case ".xlsx":
                    return "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet";
                case ".csv":
                    return "application/vnd.ms-excel";
                case ".pdf":
                    return "application/pdf";
                case ".xml":
                    return "application/xml";
                case ".sdxl":
                    return "application/xml";
                default:
                    return "application/octet-stream";
            }
        }

A: The short answer is, you can't do this with the WebDev development web server since it doesn't support the integrated pipeline mode.  The solution is to use IIS Express to host the page.  You still get full debugging integration and so on.
Handy links:

*

*Enabling IIS Express support in VS2010

*Visual Studio 2010 SP1 for Web Developers (specifically the IIS Express 7.5 Support section)

*Setting up Visual Studio 2010 and IIS Express
Or just google VS2010 IIS Express for any number of other helpful hints.

redpajama_set_name: RedPajamaStackExchange


how to find and select an item in treeview.
How can I find and select an item in treeview control. This treeview control is not created by AutoIt. This is windows treeview. For ex:- treeview we get on the left side when we run "inetmgr.exe".
You mind pointing out where in the help file ControlTreeView is.
Sorry folks. ControlTreeView in only on TODO list.
So you must use Auto3Lib UDF.
Thanks folks that worked for me... Now I have another query..
how can I select table elements shown in right pane of "inetmgr.exe" window?
Window info tool says that it is Static and it is hidden.
I don't think you're looking at the correct control. Static is the control name for a label.
When I rollover the mouse over the list view area of inetmfr then window info tool shows Static1 against ClassNameNN field.
Well, if that's true, then you're pane is a label.


Below are great tips on finding the right property to build a custom home.
Unless you have cash, you must obtain pre-approval by an approved lender - this step is vital, especially when you find land and want to make an offer. If another offer comes in that does have pre-approval, the seller will accept the offer from the pre-approved buyer. In addition, by getting pre-approval, we will only focus on property that is within your budget which will save time and heartache.
Search for desired land. When acceptable land is found, asking the listing agent questions about the land, including, by not limited to: right of ways, easements, covenants, or other deed restrictions or encroachments? Homeowners/association fees? Wetlands, floodplain? Access to power, gas, water, or sewer? Potable water? Percolation test needed? Septic design? Boundaries clearly and accurately marked?
MAKE AN OFFER RIGHT AWAY BEFORE IT'S GONE! A good deal doesn't last long.
Make contact with the county/city and utility providers on the unknowns. You will need to have the APN (parcel #) of the property you need information on. Always best to get information in writing.
Health Department - Finding out about water and waste options is vital, including connection costs. For septic systems, has there been a percolation test, what type of system is needed. Water quality. How deep will a well need to be drilled? Areas of high water table or poor soil for septic.
Planning/Building Commission - Checking on zoning restrictions, building size requirements or style, permit fees and costs for building, electric, well, septic, etc.) Any special assessments or impact fees?
Assessor's Office - Tax information and more information on zoning, flood areas, wetlands, restrictions, road footage or easements.
Title Company - Getting a clear title. Understanding easements or lack of easements that may affect your property use and availability of all utilities.
Surveyor and Civil Engineers - Where are the property lines and how is soil stability? Knowing these things can drastically change the type of home you put on the property as well as the type of foundation that is necessary.
Utility Companies - What are connection costs and total costs to bring power to home, including fees, trenching, and meter installation. Can natural gas be brought to the home, including fees, trenching, and meter? May need to get outside bids for trenching and extending lines.
Internet/Phone - What are your options for phone, cell phone service, cable, satellite, high-speed internet? Do you work from home?
Well Driller - Contacting a well driller if a well is needed on costs to install the well, labor and materials including pump, trenching, backfill, etc. Testing of water for quality. What sort of water treatment systems are commonly used and cost to install and maintain.
Lawyer - Seeking the advice of a lawyer on any questions that remain unclear after speaking to the seller, the agent, or city/county officials or speaking to the appropriate professional.
Prior to escrow or during the inspection period, make sure you negotiate the proper time you need. Based on contingencies and the above information, make a decision on whether to purchase.
Your Search For the Right Lot Is My Priority!
Have questions? Call me anytime at (530) 338-7788.
Khrystia at Umpqua Bank is a preferred lender for HiLine Homes. If you've been dreaming of wide-open spaces, Khrystia is here to help. As a large-acreage specialist, she knows how to navigate the complexities of the associated loan programs. Throughout her 18 years in the industry, Khrystia has also developed strong expertise in other types of financing options, including conventional, FHA, VA, construction and first-time buyer loans. Her knowledge, skill and world-class service have earned her numerous industry awards, including Portland Five Star Mortgage Professional and Construction Specialist of the Year.
Chad Jackson at Alliance Capital Partners is also one of HiLine Homes' preferred lenders. An owner and partner with Alliance Capital Partners, Chad provides home financing with a personal touch for homebuyers.
As a mortgage professional in the wholesale channel, Chad has working relationships with several local banks. With the ability to compare rates between ten different options, his clients benefit from a rate and fee structure that is consistently more competitive than the larger, nationally recognized lending institutions.
His knowledge and experience in the mortgage industry allows him to complete the mortgage planning process with his clients in-house instead of relying on banks and lenders who do not even operate in our time zone. I make the process seamless and easy to understand, identifying and communicating everything that is needed on the front end of each transaction.


Case Study: The application note will summarize the key benefits of the c-BMS and how it can be applied into a typical Light Electric Vehicle (LEV) application.
conventional Lead Acid (L/A) batteries with modern Lithium Ion based technology, is rapidly increasing.
The state of the art lithium battery solution was supplied by LiTHIUM BALANCE The lithium ion battery solution provides cost and energy savings, extended operation and improved performance.
Case Study: Tennant UK launched the World's first Li-Ion battery powered, street sweeper using an innovative BMS developed by LiTHIUM BALANCE.


What is the campus role in Federal Relations?
Each Chancellor shall designate a campus officer to carry out the function of a Federal Relations Officer (FRO). Collectively, campus federal relations officers make up the UNC System's Federal Relations Council.
Coordinate visits by covered federal government officials to the campus or affiliated campus program.


A basic understanding of electrical safety principles is valuable to all environmental health professionals. This practical workshop is designed to provide an overview of the common electrical safety issues, including electrical installation faults and common misconceptions. The electrical installation condition report awareness (EICR) provides a detailed insight into EICR requirements, compliance codes and the factors that make installation safe. Gain the knowledge to carry out visual electrical checks with confidence.
The CIEH Excellence Awards is the premier international awards scheme for recognising and celebrating excellence and achievement in environmental health.


Customer Service and Real Estate is in Mona's DNA with numerous family members in the profession. Mona relocated to make Tucson her home in 2013 with her husband Lewis and two children at the University of Arizona. Go Wildcats!
Mona was born in Madrid, Spain and spent her formative years in Bangkok, Thailand. Her family of 5 went around the world in 60 days with 2 trunks! She brings her extensive cultural experience as well as over 15 years as a Sales Manager for the top Hotel brands that include Ritz Carlton, Marriott and Starwood.
As a professional actress and writer, she starred in the movie Million Dollar Mystery which filmed all over Arizona and so began her love affair with the awesome beauty of the desert.
Mona is a realtor to the stars and gives all her clients the red carpet treatment with over 40 years of combined experience on the Ledford, Clark and Clark Team!
Mona focuses on the Catalina Foothills, the Eastside, Central and Downtown Areas.
Mona's specialties are buyers, sellers, relocation assistance and property management.


text:
\section{Introduction}

In this paper, we study the effect of non-normalized text on natural language processing (NLP).
Non-normalized text includes non-canonical word forms,
noisy word forms, and word forms with "small" perturbations,
such as informal spellings, typos, scrambled words.
Compared to normalized text, the variability of non-normalized text
is much greater and aggravates the problem of data sparsity.

Non-normalized text dominates in many real world applications.
Similar to humans, ideally NLP should perform reliably and robustly also under
suboptimal or even adversarial conditions, without a significant
degradation in performance.
Web-based content and social media are a rich source for
noisy and informal text.
Noise can also be introduced in a downstream NLP application
where errors are propagated from one module to the next.
For example, speech translation where the machine translation (MT)
module needs to be robust against errors introduced by the
automatic speech recognition (ASR) module.
Moreover, NLP should not be vulnerable to adversarial examples.
While all these examples do not pose a real challenge to an experienced human reader,
even "small" perturbations from the canonical form can make a state-of-the-art NLP system fail.

To illustrate the typical behavior of state-of-the-art NLP
on normalized and non-normalized text,
we discuss an example in the context of neural MT (NMT).
Different research groups have shown that NMT can generate natural and fluent
translations \cite{DBLP:journals/corr/BentivogliBCF16},
achieving human-like performance in certain settings \cite{DBLP:journals/corr/WuSCLNMKCGMKSJL16}.
The state-of-the-art NMT engine \emph{Google Translate}\footnote{https://translate.google.com/, February 2017},
for example, perfectly translates the English sentence

\begin{small}\noindent
I used my card to purchase a meal
on the menu and the total on my
receipt was \$ 8.95 but when I went
on line to check my transaction it
show \$ 10.74 .
\end{small}

\noindent
into the German sentence

\begin{small}\noindent
Ich benutzte meine Karte , um eine
Mahlzeit auf der Speisekarte zu
kaufen und die Gesamtsumme auf
meiner Quittung war \$ 8,95 , aber
als ich online ging , um meine
Transaktion zu überprüfen , zeigt
es \$ 10,74 .
\end{small}

\noindent
Adding some noise to the source sentence by swapping
a few neighboring characters, e.g.,

\begin{small}\noindent
I used my card ot purchase a meal
no the mneu and the total no my
receipt was \$ 8.95 but whne I went
on line to check ym transaction it
show \$ 1.074 .
\end{small}

\noindent
confuses the same NMT engine considerably:

\begin{small}\noindent
Ich benutzte meine Karte ot Kauf
eine Mahlzeit nicht die Mneu und
die insgesamt nicht meine Quittung
war \$ 8,95 aber whne ging ich auf
Linie zu überprüfen ym Transaktion
es \$ 1.074 .
\end{small}

\noindent
By contrast, an experienced human reader can still
understand and correctly translate the noisy sentence
and compensate for some information loss
(including real word errors such as "no" vs. "on", but rather not "10.74" vs. "1.074"),
with little additional effort and often not even noticing "small" perturbations.

One might argue that a good translation should in fact
translate corrupted language into corrupted language.
Here, we rather adopt the position that the objective
is to preserve the intended content and meaning of a sentence regardless of noise.

It should be noted that neural networks with sufficient capacity,
in particular recurrent neural networks, are universal function approximators \cite{Schafer:2006:RNN:2125268.2125345}.
Hence, the performance degradation on non-normalized text is not so much
a question whether the model can capture the variability but rather how to train a robust model.
In particular, it can be expected that
training on noisy data will make NLP more robust,
as it was successfully demonstrated for other application domains including
vision \cite{DBLP:journals/taslp/CuiGK15} and
speech recognition \cite{multistyle-google:2016}.

In this paper, we empirically evaluate the robustness of
different models (convolutional neural networks, recurrent neural networks,
non-neural models),
different basic units (characters, byte pair encoding units), and
different NLP tasks (morphological tagging, NMT).
Due to easy availability and to have more control on the experimental setup
with respect to error type and error density,
we use synthetic data generated from existing clean corpora by perturbing
the word forms. The perturbations include character flips and swaps of
neighboring characters to imitate typos, and word scrambling.

The contributions of this paper are the following.
Our experiments confirm that (i) noisy input substantially degrades the output of models trained on clean data.
The experiments show
that (ii) training on noisy data can help models achieve performance on noisy data similar to that of models trained on clean data tested on clean data,
that (iii) models trained noisy data can achieve good results on noisy data almost without performance loss on clean data,
that (iv) \emph{error type} mismatches between training and test data can have a greater impact than \emph{error density} mismatches,
that (v) character based approaches are almost always better than byte pair encoding (BPE) approaches with noisy data,
that (vi) the choice of neural models (recurrent, convolutional) is not as significant, and
that (vii) for morphological tagging, under the same data conditions, the neural models outperform a conditional
random field (CRF) based model.

The remainder of the paper is organized as follows.
Section \ref{sec:related} discusses related work.
Section \ref{sec:noise} describes the noise type
and Section \ref{sec:model} briefly summarizes the modeling approaches
used in this paper.
Experimental results are shown and discussed in Section \ref{sec:experiments}.
The paper is concluded in Section \ref{sec:conclusion}.

\section{Related Work}\label{sec:related}
A large body of work on regularization techniques to learn robust representations and models exists.
Examples include $\ell_2$-regularization, dropout \cite{DBLP:journals/corr/abs-1207-0580},
Jacobian-based sensitivity penalty \cite{Dauphin-et-al-NIPS2011,DBLP:journals/corr/LiCB16a},
and data noising.
Compared to other application domains such as vision \cite{lecun-98,DBLP:journals/corr/GoodfellowSS14} and
speech \cite{lippman1987,tuske2014data,DBLP:journals/taslp/CuiGK15,multistyle-google:2016},
working on noisy data \cite{Gimpel:2011:PTT:2002736.2002747,Derczynski:2013,DBLP:journals/corr/Plank16}
and in particular data noising \cite{Yitong2017},
 do not have a long and extensive history in NLP.

While invariance transformations such as rotation, translation in vision
or vocal tract length, reverberation, and noise in speech have all been harnessed,
we do not have a good intuition on useful perturbations for written language yet.
Label dropout and flip (cf. typos) have been proposed both on
the byte-level \cite{DBLP:journals/corr/GillickBVS15}  and
the word-level \cite{1703.02573}.
Syntactic and semantic noise for semantic analysis was studied in \cite{Yitong2017}.
From a human perception perspective,
word scrambling may be of interest
\cite{Rawlinson:1976,Rayner:2006}.

The arbitrary relationship between the orthography of a word and its
meaning in general is a well known assumption in linguistics \cite{Saussure:1916}.
However, the word form often carries additional important information.
This is, for example, the case in morphologically rich languages or in non-normalized text
where small perturbations result in similar word forms.
Recently, sub-word units have attracted some attention in NLP to handle rarely and unseen words
and to reduce the computational complexity in neural network approaches
\cite{ling-EtAl:2015:EMNLP2,DBLP:journals/corr/GillickBVS15,DBLP:journals/corr/SennrichHB15,DBLP:journals/corr/ChungCB16,heigold2017}.
Examples for sub-word units include BPE based units \cite{DBLP:journals/corr/SennrichHB15},
characters \cite{ling-EtAl:2015:EMNLP2,DBLP:journals/corr/ChungCB16,heigold2017} or
even bytes \cite{DBLP:journals/corr/GillickBVS15}.
A comparison of BPE and characters for machine translation regarding grammaticality
can be found in \cite{DBLP:journals/corr/Sennrich16}.

\section{Noise Types}\label{sec:noise}
In this work, we experiment with three different noise types:
character swaps, character flips, and word scrambling.
Character flips and swaps are rough approximations to typos.
Word scrambling is motivated from psycholinguistic studies \cite{Rawlinson:1976}.
This choice of noise types allows us to automatically generate noisy text with different type and density distributions from existing properly edited "clean" corpora.
Using synthetic data is clearly suboptimal,
but we use synthetic data because of their easy availability and
because it gives us better control on the experimental setup.

\paragraph*{Character swaps}
This type of perturbation randomly swaps two neighboring characters in a word.
The words are processed from left to right.
A swap is performed at each position with a pre-defined probability.
Hence, movements from the left to the right beyond neighboring characters are possible.
A character-swapped version (10\% swapping probability)
of the clean example sentence in the introduction may look like this:

\begin{small}\noindent
I used my card ot purchase a meal
no the mneu and the total no my
receipt was \$ 8.95 but whne I went
on line to check ym transaction it
show \$ 1.074 .
\end{small}

\paragraph*{Word scrambling}
Humans appear to be good at reading scrambled text\footnote{
\url{http://www.mrc-cbu.cam.ac.uk/people/matt-davis/cmabridge/}, note the word scramble in the URL!}.
In a word scramble, the characters can be in an arbitrary order.
The only constraint is that the first and last character be at the right place.
In particular, all word scrambles are assumed to be equally likely.
A scrambled version of the clean example sentence in the introduction may look like this:

\begin{small}\noindent
I uesd my card to pchasure a mael
on the mneu and the ttaol on my
repciet was \$ 89.5 but wehn I went
on line to chcek my tanrsactoin it
sohw \$ 1.074 .
\end{small}

\noindent
Clearly, some word scrambles are easier than others.
Word scrambling approximately includes character swaps.

\paragraph*{Character flips}
This type of perturbation randomly replaces a character with another character at a pre-specified rate.
Characters are drawn uniformly, but special symbols (e.g., end of stream) are excluded.
We do not assume any correlation across characters.
A character-flipped version (10\% flipping probability)
of the clean example sentence in the introduction may look like this:

\begin{small}\noindent
I used my car> to purch.s' a meal
on the menu and the total on my
receipv tas \$ 8.95 but whe3 = wen+
on lin4 to chece my tran\&awtion it
shzw \$ 10.74 .
\end{small}

Character flips preserve the order of characters but replace some information with random information,
whereas character swaps and word scrambling relax the order of characters but do not add
random information.

Other simple perturbations include randomly removing or adding (in particular, repeating) characters.

In the experimental section, we will consider different noise distributions
(one type of noise, combination of noise types) and
mismatched noise distributions for training and testing.

A word of length $n$ with at most one character flip can have up to $nC$ different word forms,
where $C$ denotes the number of characters in the vocabulary.
Word scrambling multiplies the number of word forms by a factor of $(n-2)!$.
In general, perturbing word forms introduces a great deal of variability and data becomes much more sparse,
implying that efficient handling of rare and unseen words will be crucial.

\section{Modeling}\label{sec:model}

This section briefly summarizes the modeling approaches used in this work.

First, we address the choice of unit.
As illustrated in Table~\ref{tab:example} on an example from the UD English corpus\footnote{\url{http://dependencies.org/}},
a word-based unit does not seem to be an appropriate unit in the presence of perturbations.
Any change of the word form implies a different, independent word index.
Even worse, most perturbed word forms do not represent valid words and
are mapped to the \mbox{$<$\hspace{-0.25ex}unk\hspace{-0.25ex}$>$}-token and no word-specific information is preserved.
This suggests the use of sub-word units.
Here, we use BPE units \cite{DBLP:journals/corr/SennrichHB15} and characters as the basic units.
BPE is based on character co-occurrence frequency distributions and has the effect of representing
frequent words as whole words and splitting rare words into sub-word units
(e.g., "used" as "used", "purchase" as "purcha\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@} se").
BPE provides a good tradeoff between modeling efficiency
(i.e., the model does not need to learn for the frequent words how to assemble them)
and handling unknown words.
However, BPE may not be efficient at representing noisy word forms as
small perturbations can lead to a different  representation using different BPE units
(e.g., "used" vs. "u\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@} es\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@} d", "purcha\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@} se" vs. "p\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@} cha\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@} sure").
As the example illustrates (Table~\ref{tab:example}), perturbations tend to break
longer units into smaller units, which makes the use of whole word units less useful.
Finally, characters as the basic units have similar representations for similar word forms,
but result in longer sequences, which makes the modeling of long-range dependencies
harder and increases the computational complexity.
\begin{table*}[htbp]
\small
\caption{Clean (left) vs. scrambled (right) example sentence using a word-based (top), a BPE-based (middle), and a character-based (bottom) representation}
\begin{minipage}{\textwidth}
\begin{minipage}{0.49\textwidth}
I used my card to purchase a meal
on the menu and the total on my
receipt was \$ 8.95 but when i went
on line to check my transaction it
show \$ 10.74 .
\end{minipage}
\hspace{0.02\textwidth}
\begin{minipage}{0.49\textwidth}
I \mbox{$<$\hspace{-0.25ex}unk\hspace{-0.25ex}$>$}{}  my card to \mbox{$<$\hspace{-0.25ex}unk\hspace{-0.25ex}$>$}{}  a \mbox{$<$\hspace{-0.25ex}unk\hspace{-0.25ex}$>$}{}
on the \mbox{$<$\hspace{-0.25ex}unk\hspace{-0.25ex}$>$}{}  and the \mbox{$<$\hspace{-0.25ex}unk\hspace{-0.25ex}$>$}{}  on my
\mbox{$<$\hspace{-0.25ex}unk\hspace{-0.25ex}$>$}{}  was \$ 89.5 but \mbox{$<$\hspace{-0.25ex}unk\hspace{-0.25ex}$>$}{}  i went
on line to \mbox{$<$\hspace{-0.25ex}unk\hspace{-0.25ex}$>$}{}  my \mbox{$<$\hspace{-0.25ex}unk\hspace{-0.25ex}$>$}{}  it
\mbox{$<$\hspace{-0.25ex}unk\hspace{-0.25ex}$>$}{}  \$ 1.074 .
\end{minipage}
\vspace{1ex}
\end{minipage}
\begin{minipage}{\textwidth}
\begin{minipage}{0.49\textwidth}
I used my c\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} ard to purcha\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} se a me\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} al on the men\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} u and the to\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} tal on my recei\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} pt was \$ 8\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} .\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} 9\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} 5 but when I went on line to check my trans\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} action it show \$ 10\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} .\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} 7\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} 4 .
\end{minipage}
\hspace{0.02\textwidth}
\begin{minipage}{0.49\textwidth}
I u\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} es\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} d my c\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} ard to p\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} cha\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} sure a ma\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} el on the m\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} ne\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} u and the t\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} ta\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} ol on my rep\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} ci\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} et was \$ 8\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} 9\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} .\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} 5 but we\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} h\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} n I went on line to ch\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} c\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}
 e\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} k my t\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} on\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} tri\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} as\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} ac\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} n it so\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} h\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} w \$ 1\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} .\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} 0\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} 7\mbox{\hspace{-0.25ex}@\hspace{-0.5ex}@}{} 4 .
\end{minipage}
\end{minipage}
\begin{minipage}{\textwidth}
\begin{minipage}{0.49\textwidth}
\begin{verbatim}
I used my card to purchase a meal on the
menu and the total on my receipt was $
8.95 but when i went on line to check my
transaction it show $ 10.74 .
\end{verbatim}
\end{minipage}
\hspace{0.02\textwidth}
\begin{minipage}{0.49\textwidth}
\begin{verbatim}
I uesd my card to pchasure a mael on the
mneu and the ttaol on my repciet was $
89.5 but wehn I went on line to chcek my
tanrsactoin it sohw $ 1.074 .
\end{verbatim}
\end{minipage}
\vspace{1ex}
\end{minipage}
\label{tab:example}
\end{table*}

Noise modeling for a word-level system is straightforward as perturbed word forms are mapped to \mbox{$<$\hspace{-0.25ex}unk\hspace{-0.25ex}$>$},
i.e., noise modeling reduces to word-level label dropout (and rarely word-level label flips) \cite{1703.02573}.
This is not true for sub-word level representations, for which more detailed noise modeling will be important.

We use model architectures based on recurrent and convolutional neural networks in this work.
Assuming that a word segmentation is given, we first map the sub-word units of a word
to a word vector and then continue as for word-based approaches.
Deep neural networks are universal function approximators \cite{Schafer:2006:RNN:2125268.2125345}.
Hence, a neural network with sufficient capacity is expected to learn the variability induced by perturbations.
We compare the neural networks with a conditional random field \cite{Lafferty:2001:CRF:645530.655813}.

\section{Experiments}\label{sec:experiments}
In this section, we empirically evaluate the robustness against perturbed word forms
(Section~\ref{sec:noise}) for the two common NLP tasks morphological tagging and machine translation.

\subsection{Morphological Tagging}
We used the model configurations and setups from \cite{heigold2017}
for the morphological tagging experiments in this paper.
Training and testing was performed on the UD English data set\footnote{\url{http://dependencies.org/}}.
Figure~\ref{fig:udenglish} summarizes the results.
\begin{figure*}[htbp]
  \includegraphics[height=0.36\textwidth]{udenglish-histogram-LSTMBLSTM}
  \includegraphics[height=0.36\textwidth]{udenglish-histogram-CNNHighwayBLSTM}
  \includegraphics[height=0.36\textwidth]{udenglish-histogram-LSTMBLSTM-bpeaschar}
  \includegraphics[height=0.36\textwidth]{udenglish-histogram-MarMoT}
  \caption{Noise behavior for morphological tagging for different models and units on UD English test data.
  Upper left: character-based LSTM-BLSTM.
  Upper right: character-based CNNHighway-BLSTM.
  Lower left: BPE-based LSTM-BLSTM.
  Lower right: MarMoT (CRF).}
  \label{fig:udenglish}
\end{figure*}
We explored the three main dimensions of noise type and distribution, choice of unit, and type of model.
Noise-adaptive training means standard training on noisy input sentences
(but with correct labels: rich morphological tags or target language translation).
We distinguish the noise type and distribution used for training ("training noise type")
and testing ("test noise type").

We start our discussion with the upper left histogram in Figure~\ref{fig:udenglish}
for the character-based LSTM-BLSTM architecture.
It shows a clear performance degradation from around 95\% to around 80\% tag accuracy across
all noise types compared to when trained on clean data ("clean").
Here, we consider the noise types word scrambling ("scramble", note that all words are scrambled),
character swaps with probability 10\% ("swap@10\%),
and character flips with probability 10\% ("flip@10\%").
Bar groups 2, 3 and 4 in the upper left histogram in Figure~\ref{fig:udenglish} show that
noise-adaptive training helps in all cases, bringing the tag accuracy back to above 90\% and without greatly affecting
the accuracy on clean data.
As expected, the accuracy under matched training and test conditions is highest in all cases.
The transferability from a noise type to another depends on the noise types.
For example, noise-adaptive training for "swap@10\%" improves the accuracy on the "scramble" test condition
by approximately 10\%.
On the other hand, the "flip@10\%" test condition gets slightly worse.
This outcome may be expected because characters swaps are more closely related with word scrambling
than character flips.
The transferability does not need to be symmetric.
An example is "flip@10\%"-adaptive training which improves on the "swap@10\%" and "scramble" test conditions,
whereas we observe slight degradation in the opposite direction.
Finally, can we train a model that performs well across all these noise types as well as on clean data?
For this, we mixed different noise types at the sentence level for training ("combined"),
i.e., a clean sentence, followed by a sentence with scrambling inside words,
followed by a sentence with swapped characters inside words,
followed by a sentence with flipped characters inside words, and so forth in the training data.
The test data, by contrast, was pure clean ("clean"), scrambled ("scramble"), swapped ("swap@10\%"), or flipped ("flip@10\%") data.
According to the results summarized in the final group of bars in the upper left histogram in Figure\ref{fig:udenglish},
this is approximately possible.
This result again suggests that noise strongly impacts on models trained on clean data (curve for 0\% character flips),
and that injecting noise at training time is critical but the exact noise distribution
is not so important in this case.

The upper left and lower left histograms in Figure~\ref{fig:udenglish} differ in the choice of unit on the input text side,
"char-LSTM-BLSTM" uses characters and "bpe-LSTM-BLSTM" 2,000 BPE units\footnote{
In neural MT, BPE size is usually around 50,000.
For morphological tagging we adjust the number of BPE units according to the amount of data:
the UD English training data roughly includes 2,000 unique words with at least 10 occurrences.
For our NMT based experiments in Section~\ref{sec:mt}, we use the customary BPE setting in NMT.}.
The overall behavior is similar, but characters seem to degrade more gracefully
than BPE units for mismatched noise conditions
(compare bar columns 2, 3 and 4 between the upper left and lower left histograms in Figure~\ref{fig:udenglish}).

Finally, we explore how different models behave on noisy input
(compare bar columns 2, 3 and 4 between the upper left and lower left histograms in Figure~\ref{fig:udenglish}).
For this, we compare a char-LSTM-BLSTM,
a char-CNNHighway-BLSTM (same as char-LSTM-BLSTM but uses a convolutional neural network
to compute the word vectors) \cite{heigold2017},
and a conditional random field \cite{muller-schuetze:2015:NAACL-HLT}
including word-based features and prefix/suffix features up to length 10 for rare words
(we used MarMoT\footnote{\url{http://cistern.cis.lmu.de/marmot/}} for the experiments).
The upper left, upper right and lower right histograms in Figure\ref{fig:udenglish} show that
the qualitative behavior of the three models is very similar.
char-LSTM-BLSTM and char-CNNHighway-BLSTM achieve similar performance.
One might speculate if char-LSTM-BLSTM is slightly better at flip@10\% and char-CNNHighway-BLSTM at swap@10\% and word scrambling,
but the differences are most likely not significant.
MarMoT's tag accuracies for all noise conditions is worse by 5-10\%.

As indicated above, Table 1 shows results on English morphological tagging.
In a suite of experiments (not shown here in full detail for reasons of space)
we have confirmed similar overall results for morphologically-richer languages such as German.
Morphological tagging for German is much harder than for English:
while the English UD training data exhibit 119 distinct types of sequences of POS tags
followed by morphological feature descriptions,
the TIGER training data for German exhibit 681 distinct types of such sequences.
To give one result from our German experiments,
Figure~\ref{fig:tiger} shows the dependency of the test accuracy
on the amount of character flips in the test data, for various amounts of character flips in training.
Assuming an average word length of 6 characters,
10\% character flips correspond with one typo in every second word,
20\% character flips with one typo per word, and
30\% character flips with two typos per word.
This result suggests that injecting noise at training time is critical,
whereas the test accuracy does not depend so much on the exact amount of training noise
(curves for 10\%, 20\% and 30\% character flips)
and that models trained on noise injected data are still able to tag clean data with
almost no loss in performance compared to a model trained on clean data only.
\begin{figure}
  \includegraphics[width=1.0\columnwidth]{noise}
  \caption{Effect of amount of character flips in training and testing for morphological tagging on German TIGER test data}
  \label{fig:tiger}
\end{figure}

Morphological tagging is  a sequence-to-sequence labelling task,
where (to a first approximation) the number and order of elements in the two sequences is the same
(each word/token is paired with a POS tag plus morphological description).
Translation is arguably a much harder task as
it often relates sequences of different lengths with possibly substantial changes
in the order of corresponding words/tokens between source and target and,
compared to morphological tagging, much larger sizes of output vocabularies.
In a second set of experiments, we explore the impact and handling of noise in the input to machine translation.

\subsection{Machine Translation}\label{sec:mt}
Our NMT setup is based on the setup in \cite{heigold2016}.
We use BPE units or characters as the basic units at the source side
and always BPE units at the target side
(following common practice in our experiments we use a BPE size of 50,000),
resulting in the two model configurations "BPE-BPE" and "char-BPE".
For the character-based encoder,
we assume the word segmentation and
map the word string consisting of characters or BPE units
to a word vector by a two-layer unidirectional LSTM.
The baseline model ("clean") is trained on the German-English (DE-EN) parallel corpora
provided by WMT'16\footnote{\url{http://www.statmt.org/wmt16/translation-task.html}}.
Results for the newstest2016-deen data set are shown in Table~\ref{tab:mt-bleu}.
\begin{table}[htbp]
   \centering
   \topcaption{BLEU on newstest2016-deen for clean and noisy NMT and different test noise types}
   \begin{tabular}{@{} lrrrr @{}}
      \hline
      Test & \multicolumn{2}{c}{BPE-BPE} & \multicolumn{2}{c}{char-BPE}\\
      noise & & noise- & & noise- \\
      type & clean & adapted & clean & adapted \\
      \hline
      clean      & 31.6  & 30.4  & 30.7  & 30.6  \\
      swap@5\%      & 19.8  & 25.0  & 25.0  & 29.2  \\
      scramble   &  3.6  &  9.4  &  5.4  & 20.0  \\
      flip@5\%      & 16.1  & 22.5  & 21.7  & 27.1  \\
      noisy      & 21.9  & 25.6  & 21.1  & 28.5  \\
\hline
   \end{tabular}
   \label{tab:mt-bleu}
\end{table}
For noise-adaptive training, we perform transfer learning on the perturbed source sentence-target sentence pairs
("noise-adapted").
For training, we choose the following sentence-level noise distribution:
50\% clean sentences, 20\% sentences with character swaps (5\% swap probability),
10\% sentences with word scrambles,
and 20\% sentences with character flips (5\% flip probability).
We refer to this noise distribution to as "noisy."
Beside this "noisy" noise distribution, we also use mismatched noise conditions at test time,
consisting of a single noise type only, referred to as
"clean", "swap@5\%", "scramble", and "flip@5\%".

The baseline's performance drop for noisy test data is drastic and clearly depends on the noise type.
Word scrambling seems to be the hardest noise type, for which BLEU goes down from around 30 to around 5
for BPE-BPE and char-BPE.
Overall, however, the results suggest that the char-BPE baseline degrades much more gracefully than the BPE-BPE baseline.

The results in Table~\ref{tab:mt-bleu} show that noise-adaptive training can considerably improve the performance
on noisy data and the gap between clean and noisy conditions can be almost closed for the "easy" noise conditions.
Similar to the baseline, char-BPE tends to be less sensitive to mismatched noise conditions.
This may be best seen from the fact that char-BPE performs better or no worse than BPE-BPE for all noise conditions.
Moreover, noise-adaptive training does not affect BLEU for char-BPE (30.7 vs. 30.6) but there is a small
performance penalty for BPE-BPE (31.6 vs. 30.4).
Furthermore, the "noisy" BLEU is the highest among the noisy conditions for BPE-BPE while the "swap@5\%" BLEU is the
best for char-BPE.

We show an example for the different noise types and source representations in Table~\ref{tab:nmt-examples}.
The example reflects the general findings based on BLEU scores (Table~\ref{tab:mt-bleu}).
The example also highlights the potential difficulty of correctly translating proper names in noisy conditions.
\begin{table*}[t]
   \centering\small
   \topcaption{Example sentence for different noise types (clean, character swaps, word scrambling, character flips)
   and NMT configurations (BPE/characters and standard training/noise-adaptive training)}
   \begin{tabular}{@{} ll @{}}
   \hline\hline
   source &
   Herr Modi befindet sich auf einer fünftägigen Reise nach Japan , um die wirtschaftlichen Beziehungen mit \\
   (clean) & der drittgrößten Wirtschaftsnation der Welt zu festigen . \\
\hline
unadapted &
Mr Modi is on a five-day trip to Japan to consolidate economic
relations with the world 's third largest \\
(BPE-BPE) &  economies . \\
noise-adapted &
Mr Modi is on a five-day trip to Japan to consolidate economic relations with the third largest economic \\
 (BPE-BPE) &  nation in the world . \\
\hline
unadapted &
Mr Modi is on a five-day trip to Japan to consolidate economic
relations with the world 's third largest \\
(char-BPE) &  economy . \\
noise-adapted &
Mr Prodi is on a five-day trip to Japan in order to consolidate economic relations with the world 's third \\
 (char-BPE) &  largest economy. \\
\hline\hline
source &
Herr Modi befindet sich auf einer fünftägigen Reise nach Japan, um die wirtschaftlichen Beziehungen mit \\
 (swap@5\%) & der rdtitgrößten Wirtschaftsnation der Welt zu festiegn.\\
 \hline
unadapted &
Mr Modi is on a five-day trip to Japan to entrench economic relations with the world 's most basic economic \\
 & nation .\\
noise-adapted &
Mr Modi is on a five-day trip to Japan to establish economic relations
with the world 's largest economic \\
 (BPE-BPE) & nation . \\
 \hline
unadapted &
Mr Modi is on a five-day trip to Japan to establish economic relations
with the world's largest economy. \\
 & \\
noise-adapted &
Mr Prodi is on a five-day trip to Japan in order to consolidate economic relations with the world's third \\
 (char-BPE) &  largest economy. \\
\hline\hline
source &
Hrer Modi bfdneeit scih auf eienr fggnefüiätn Reise ncah Jpaan , um die wctathhilsfecirn Buzegehnein mit \\
(scramble) & der drtiößettrgn Wsfactohtairsntin
der Welt zu fgteesin . \\
\hline
unadapted &
Hrer modes Bfdneeit scih on eienr fggnefün journey ncah Jpaan to get
the wctathsusfecirn Buzehno with the \\
 & drone Wsfactohtairsntin in the world . \\
noise-adapted &
Mr Modi is looking forward to a successful trip to Jpaan in order to
find the scientific evidence with the  \\
(BPE-BPE) & world 's largest economy in the world .\\
\hline
unadapted &
Hear is a member of the United States of America and the United States
of America . \\
 & \\
noise-adapted &
Mr Prodi is working on a fictitious journey to Japan in order to
address economic relations with the world 's \\
(char-BPE) & third largest economy . \\
\hline\hline
source &
Herr Modi befindet sicC 0uf einer fünftägigen Reise nach Japan , u" die
wirtsch\_átlichen Beziehungen mi4 \\
(flip@5\%) &  dLr drittgrößten Wirtschaftsn,tion der Welt zu f?stigen .\\
\hline
unadapted &
Mr. Modi is located at sicC 0uf a five-day trip to Japan , u" the
wiring relations mi4 dLr third-largest \\
& economy of the world .  \\
noise-adapted &
Mr Modi is on a five-day trip to Japan to promote economic relations with the world 's third largest \\
(BPE-BPE) & economy . \\
\hline
unadapted &
Mr Modi is going to Japan on a five-day trip to Japan to fudge economic relations with the world's third \\
&  largest economy . \\
noise-adapted &
Mr Prodi is on a five-day trip to Japan to consolidate economic
relations with the world 's third largest \\
(char-BPE) &  economy . \\
\hline\hline
\end{tabular}
\label{tab:nmt-examples}
\end{table*}

\section{Conclusion}\label{sec:conclusion}
In this paper, we presented an empirical study on morphological tagging
and machine translation for noisy input.
Mostly as expected from other application domains such as vision and speech,
we found that state-of-the-art systems are
very sensitive to slightly perturbed word forms that do not pose a challenge to humans and
that injecting noise at training time can improve the robustness of such systems considerably.
The best results were observed for matched training and test noise conditions
but generalization across certain noise types and noise distributions is possible.
Character-based approaches seem to degrade more gracefully compared with BPE-based approaches.
We observe similar overall trends across tasks (morphological tagging and machine translation) and languages (English and German for morphological tagging).
The results in this paper are promising but should be taken with a grain of salt
as we used augmented data based on a limited number of idealized perturbation types.
Future work will aim at a better comprehension of relevant and hard or even adversarial
perturbations and noise types (including noisy sentence structure)
in language and testing on real noisy user input.
Moreover, the observation that the lower the BPE size is, the closer BPE is to character based encoding
and the higher the BPE size is, the closer BPE is to word based approaches,
will allow us to tune the system for the optimal granularity.
A reasonable assumption is that the denoising is task-independent and could be
trained independently of the actual NLP task,
or shared across NLP tasks and jointly optimized.

redpajama_set_name: RedPajamaArXiv


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Additional Charges From This Dealer: USD $0
Sold by choicedealsnc2017 to ElliottGrier for $8,500
This 2003 Audi RS6 is finished in Mugello Blue over black leather and is one of 1,200 C5 RS6s imported to the US for the model year. The twin-turbocharged 4.2L V8 sends power to all four wheels through a 5-speed Tiptronic gearbox. Recent maintenance included an oil service and replacement of the comfort control module and passenger-side intercooler, as well as new brake pads, mufflers, and battery. The car spent time in Texas, Florida, and Colorado, and it received a new timing belt in 2012 at 117k miles and a rebuilt transmission the following year at 122k miles according to the Carfax report. This RS6 is now being offered for sale at no reserve by the North Carolina-based dealer with a receipt from the recent service, a clean Carfax, and a clear Colorado title.
Mugello Blue paint is shown up close and from a variety of angles in the gallery below. The seller notes some clear coat fading on the roof, and a chip is present on the lower front valence. Aftermarket tint has been applied to the rear windows, and the clean Carfax is viewable below.
RS6 features include a Dynamic Ride Control (DRC) system, Brembo 8-piston calipers, and cross-drilled rotors. New brake pads and mufflers were recently installed. Factory 9-spoke alloy wheels show some curb rash and wear a matching set of Continental Control Contact tires with 2014 and 2015 date codes.
The interior is trimmed in black leather and features RS-embossed powered and heated seats. Carbon fiber trim accents the dash and door panels, and the factory CD stereo is retained but not functional. The dash cupholder and key fob functions are also inoperable. The digital display shows no pixelation issues in provided photographs, and 139k miles are indicated on the odometer.
The twin-turbocharged 4.2-liter V8 was developed in conjunction with Cosworth and produced 444 horsepower when new. The comfort control module (CCM), passenger-side intercooler, battery, and small radiator fan were recently replaced, and a receipt from the work is provided in the gallery.
Power is sent from the 5-speed Tiptronic automatic transmission to all four wheels through a Torsen-based Quattro all-wheel drive system. The timing belt was replaced in 2012 at 117k miles and the transmission was rebuilt in 2013 at 122k miles per the Carfax report below. According to the seller, the left outer CV boot has a tear and the lower control arm bushings could use replacement. Additional lift photos are viewable in the gallery.
The clean Carfax report lists no issues and shows history in Texas, Florida, and Colorado.
Winning Bid USD $8,500 by ElliottGrier
Auction Ended October 9, 2018 at 1:45PM PT
Bids 4