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All Age Gaming Home Page > WHO IS HUMANITY'S TRUE ENEMY? - Binary Domain Announced!
WHO IS HUMANITY'S TRUE ENEMY? - Binary Domain Announced!
2nd December 2010 - SEGA Europe Ltd. and SEGA America Inc., are pleased to announce Binary Domain, an original squad-based shooter by Toshihiro Nagoshi, the creator behind some of SEGA's most well-known video game franchises, including the critically acclaimed Yakuza series. Binary Domain puts players in the middle of a fast-paced and intense battle for humanity in robot-invaded 2080 Tokyo. Fighting through the derelict lower levels of the city, players control an international peace-keeping squad that soon starts to question their surroundings and the choices they made. Are the robots becoming more human, or are humans becoming more like machines?
"What I wanted to create this time was a dramatic and energetic sci-fi shooter. When you hear sci-fi you may think of cold, clinical environments but with Binary Domain I wanted to combine this with a deep human drama" commented Toshihiro Nagoshi. "The keyword we have in mind for this project is "Life". I wanted to make something that will be accepted by both the Japanese and Western markets and this fundamental theme is something everyone knows, but which the full extent of can be difficult to grasp."
Binary Domain's central theme of life is also key to the way the innovative AI system is being developed. The highly advanced AI will make the battlefield a more compelling experience than ever before and players will have to plan their moves strategically and carefully in order to progress through the hostile environments of the game.
"We are excited and proud to have Toshihiro Nagoshi working on Binary Domain," commented Gary Knight, Senior Vice President of Marketing at SEGA of Europe and SEGA of America. "The game is being developed in Japan yet it is designed for a global audience and will take players into an exhilarating story wrapped into a unique, action-packed shooter experience in a fresh new setting."
Binary Domain is being developed by SEGA Japan and Yakuza-creator Toshihiro Nagoshi for the Xbox 360 video game and entertainment system and the PlayStation 3 computer entertainment system. For more information on SEGA games please visit www.sega.com.
Sega | Binary Domain | Xbox 360 | Playstation 3 | Press Release


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30 Fascinating & Offbeat Things to do In The Czech Republic
ByNikita AswaniJanuary 11, 2021403
Visit the Czech Republic, a small but spectacular country in Central Europe. You get the best places to visit in the Czech Republic with monasteries, historic homes, cultural sites, and medieval architecture. Home to beautiful natural environs, forests and mountains, there are plenty of things to do for a traveller in the Czech Republic. The capital city Prague with plentiful Prague tourist spots is a marvellous destination. Visit renaissance architecture, baroque-style churches, classy museums, bridges and more. We take you through 30 amazing & offbeat things to do in the Czech Republic.
Travel 101: Top Things To Do In The Czech Republic
1. Be Spooked at the Sedlec Ossuary
Visit the famous bone church, a Czech Republic tourist place. Located in the small town of Kutna Hora, about 80 km from Prague. The Roman Catholic Chapel uses only human bones for interior decor. It also has a chandelier made only of human bones. The chapel commemorates people who died during the wars in Europe and plagues. Visit the Cathedral of Saint Barbara, take a cave mine tour and enjoy a beautiful hill-view of the town.
Timings: 8.00 am to 6.00 pm
Average Price: $4.50
Pro Tip: Grab a fancy lunch at the 400-year-old Dacicky Restaurant for awesome Czech cuisine.
2. Visit the Largest Ancient Castle in the World, Prague Castle
The Guinness Book of World Records claims the Prague Castle as the most ancient castle in the world. The castle is a popular Prague tourist attraction. The Bohemian Crown Jewels are protected in the secret room here. The structure is the official residential building of the Czech Republic. It was the seat of power including Holy Roman Emperors and Kings of Bohemia.
Pro Tip: Take a guided tour that is available in many languages.
3. Walk Across the Charles Bridge
This fantastic thing to do in Prague is a fun-filled activity. The 14th-century bridge is a great place to visit in Prague. Walk across the bridge, one of the most memorable things to do in Prague, because the best things in life are free. The bridge was commissioned by Charles IV in 1357 as the older bridge was washed away in the floods.
Timings: 6.00 am to 12 pm
Average Price: $11.25 (family ticket)
Pro Tip: Experience the beauty of the bridge at dawn for the best view.
4. Go Treasure Seeking at the St Vitus Cathedral
One of the top Prague attractions, St Vitus Cathedral was completed in 1929. Witness a treasure trove of attractions, a great place to visit as a Czech Republic tourist. The castle grounds also have the splendid Chapel of St Wenceslas and the tomb of St John of Nepomuk. The Vitus Cathedral tour is a top-rated experience that lasts about 2.5 hours.
Average Price: $10.00
Pro Tip: Witness the magnificent art Nouveau stained glass in the chapel.
5. Visit the Remarkable Cesky Krumlov Castle
One of the best places in the Czech Republic is the Cesky Krumlov Castle. Take a day trip from Prague to reach Cesky Krumlov town in about 2.5 to 3 hours. The castle complex will keep you amazed as you wander through fine old palaces, castle courts, and splendid gardens. Take a guided tour as this Czech Republic tourist attraction has 40 buildings to explore. The castle is listed as a national heritage site and is one of the best tourist places in the Czech Republic.
Timings: 9.00 am to 6.00 pm (last tour of the day starts at 5.00 pm)
Average Price: $18.09 (Adult)
Pro Tip: The castle was built way back in 1240 by the Vitkovci family. It is the second most visited castle after the Prague Castle.
6. Look Out for the Seven-Foot-Tall Sigmund Freud
Wander the streets of the Old Town of Prague in the urban area of Stare Mesto. A seven-foot sculpture of the famous psychoanalyst hangs from a metal beam above the cobbled streets. The popular and an offbeat Prague tourist attraction, this unusual artwork is exhibited in other cities around the world. Don't mistake the Freud sculpture for a suicide attempt. Duh!
Timings: All-day
Average Price: FREE
Pro Tip: The hanging sculpture is often exhibited even in Chicago, London, and Berlin.
7. Enjoy a Bounty of Fresh Food at the Farmers Market
A great thing to do in Prague is to visit the farmers market that happens every Saturday. Visit the beautiful river embankment beside the fortress of Vysehrad. Blend in with the locals and take delight in the aromas of freshly baked bread, seasonal vegetables, sausages, cakes, pickles, and more. The farmers market is a great experience for the best food that Prague offers.
Timings: 8.00 am to 2.00 pm (Saturday)
Average Price: Based on the purchase
Pro Tip: Naplavka Farmers Market is a popular place to visit in Prague.
8. Fall In Love With Puppets at The National Marionette Theatre
A popular thing to do in Prague is to visit puppet shops and puppet museums. Visit the Czech Republic to discover the locals' love for the puppets. They use these for entertainment at ceremonies and royal feasts. Don't miss the Prague tourist attraction for a puppet show at National Marionette Theatre. The puppetry attraction dates far back into the 12th century, a Czech love affair to behold.
Timings: 8.00 pm (two hours)
Average Price: $29
Pro Tip: Watch popular puppet performances at Theatre Spejbl & Hurvinek.
9. Stay in Awe of the Infant Jesus of Prague
A popular Prague tourist attraction, the Infant Jesus of Prague is located in Mala Strana. Here, hundreds of believers pray and bow to make wishes, that may come true. Witness the beauty of the statue encased in an ornate gilded shrine. The figure dates back to the 16th century. Feel a sense of serenity at the beautiful shrine, a lovely place to visit in Prague.
Timings: 8.30 am to 8.00 pm (Sunday) | 8.30 am to 7.00 pm (other days of the week)
Pro Tip: The shrine is also known as the Child of Prague.
10. Sip Chilled Beer at Pilsner Urquell Brewery
Enter the double-arched gates of the majestic brewery that is pictured on every label of Pilsner Urquell. Pass by stepping stones and witness the 'purest' water used in the brewing of the finest beer. You don't have to wait for the tour to end to taste the beer. Sample Pilsner Beer from a can that you can buy at the entrance. The tour is one of the best-rated Czech Republic tourist attractions.
Timings: Monday – Friday (1.00 pm, 2.45pm, 4.30 pm) | Saturday & Sunday (10.45 am, 1.00pm, 2.45 pm, 4.30 pm)
Average Price: USD 9.00 (tour in a foreign language)
Pro Tip: Visit the Brewery Museum and the Gift Shop.
11. Splash and Slide at the Aquapalace Prague
Enjoy your time among many water attractions and the longest slide in the Czech Republic. It is one of the best tourist places in the Czech Republic with wild water river winding, artificial waves, action water slides, and spins in the Spacebowl. The 9150 square metres of space is a thrilling aquapark to spend the day. Check out the sauna world, rehabilitation wellness resort, and fitness centre.
Timings: 9.00 am to 10.00 pm
Pro Tip: Stay at the Aquapalace Hotel for leisure, a great Prague place to visit.
12. Witness the Prague Astronomical Clock
A great thing to do in Prague is to visit the Prague Orloj. The medieval clock was installed way back in 1410. That makes the clock, the third-oldest in the world. Mounted in the southern wall of the Old Town Square, look for the astronomical dial that represents the position of the sun and the moon. As the local legend goes, it is a belief that the city will suffer if the clock is neglected. The clock displays twelve apostles in motion when the clocks strike an hour from 9.00 am to 11.00 pm
Timings: 9.00 am to 11.00 pm (all days except Monday) | 11.00 am to 10.00 pm (Monday)
Pro Tip: Also witness the statues of various Catholic saints that stand on either side of the clock.
13. Take a Cable Car Ride to Snezka
Visit the Krkonose National Park, a popular Czech Republic tourist attraction. Witness fabulous natural landscapes, alpine meadows, high slopes, and moorland. The UNESCO Biosphere Reserves since 1992 is one of the best places to visit in the Czech Republic. Explore the highest peak Snezka on a cable car ride. You could make Pec pod Snezkou as your base. Take a cable ride to the top of the mountain for fantastic scenic valley views.
Average Price: $17.54 (return ticket)
Pro Tip: You could also hike a trail via the scenic valley of Obri Dul.
14. Witness Awesome Natural Cascades, the Resov Waterfalls
Explore the rocky canyon. The series of cascades and rapids is the best place in the Czech Republic for natural wonders. The waterfalls are carved by the Huntava stream and are in a protected area in the southwestern regions of Nizky Jesenik Mountain range. Hike a trail through the unexplored forests past the ruins of the medieval castle. The trail along the Huntava stream is a great thing to do as a Cezh Republic tourist.
Average Price: Based on the tour
Pro Tip: The area has been protected since 1966 for its beauty and natural environs.
15. Visit the Old Jewish Ghetto
A worthwhile thing to do in Prague is to visit the Jewish quarter. It is between the Old Town and the Vltava River. Jews in Prague were exiled and were not allowed to live elsewhere. The hardships and atrocities faced by the Jews are reflected in the quarter. They were also joined by Jews from other European countries.
Pro Tip: The six synagogues in the area is a worthwhile attraction to see.
16. Witness the Astonishing Bastei Bridge
The 76-meter high arched bridge made of stone in the Saxon Park is a popular Czech Republic tourist spot. Visit the Bohemian and Saxon Switzerland National Parks to be awestruck by the region's scenery. Watch huge sandstone pillars along the Elbe River and follow a network of hiking trails. Walk across the amazing Bastei Bridge to look down onto the Elbe River. Take a boat ride through the Gorges of Kamenice or take a guided hike.
Timings: 10.00 am to 10.30 pm
Pro Tip: Sail on a gondola-style boat down the Elbe River, a highlight of your visit Czech Republic journey.
17. Be a Part of a Day Trip to Karlovy Vary
Rejuvenate in the dozen powerful hot springs at a health resort in Karlovy Vary. The springs have been a part of the attractions since 1358. Witness spectacular spring houses, fountains, and colonnades here. The film festival held in the city is one of the oldest in Europe. Visit the art centre, museums and enjoy a day trip to Karlovy Vary.
Average Price: Based on the day trip tour
Pro Tip: Look out for beautiful art objects made in Bohemian crystals.
18. Listen to Street Music at the Old Town Square
A lovely thing to do in Prague is to walk down historical streets of the Old Town Square. Admire the wonderful architecture that remains untouched since the 1oth century. Listen to music performed by street performers. Mingle with the locals and enjoy Czech cuisine at an alfresco restaurant that is packed on the streets. Merchants also line the walkways that will provide you with a lot of entertainment and fun. The square is a perfect place to spend a day, a popular Prague tourist spot.
Pro Tip: It is great to see that the many invasions of Prague have not affected the history of Old Town Square.
19. Feel like a Spy at the KGB Museum
Explore the little museum with a variety of memorabilia relating to the KGB. The museum was established by a Russian enthusiast. Look out for spy cameras, secret weapons, and types of equipment used by the KGB for interrogation. There are also photographs of Prague taken by a KGB officer in 1968. The streets of the city seem scary and empty. This is a popular Prague attraction to see.
Timings: 10 am to 5.00 pm
Pro Tip: Tour the museum with the help of the collector.
20. Check out the Lennon Wall
If you are a fan of The Beatles, then the wall is a place to visit in Prague. The wall is covered with graffiti that honours John Lennon and The Beatles. Lyrics, quotations since the 1980s fill the wall. Fans consider the wall to be a shrine for the most famous bands of all time. If you wish to pay homage to the band, the Lennon Wall is a great Prague place to visit.
Pro Tip: Lennon Wall is located near the French Embassy in Prague.
21. Cruise Down the Splendid River Vltava
A unique thing to do in Prague is a cruise on the River Vltava, the longest river in the Czech Republic. See historic buildings, monuments, and escape the buzz of the city. Enjoy a meal on board depending on the time you embark. Soak in the serenity of the riverbanks as you sail within the city.
Timings: 11.00 am to 7.00 pm (every hour)
Pro Tip: Choose a cruise of a duration of two hours or more to ensure you witness many historical buildings.
22. Go Clubbing and Revel the Nightlife of Prague
Groove to the music of jazz or classical tunes at the many live music venues in Prague. Nightlife is full of excitement with some of the best pubs. Look out for JazzDock which draws some of the best talents of local jazz musicians. Hit the Cross Club located in an industrial setting. The interiors have shafts, cranks, and gadgets which move with the music. Serious clubbers can have a smashing good time.
Timings: 9.00 pm to 12.00 am
Average Price: USD 11.86 (for two nights)
Pro Tip: Enjoy an all-night tour of Prague Pub Crawl and move from bar to bar all night.
23. Take a Funicular Ride up to Petrin Hill
Petrin Hill is on the left bank of the River Vltava. You could climb the 299 steps up the hill. Or take a funicular ride in one of the greenest spaces in Prague. The landscaped gardens and the St Michael Church are at the summit. A great thing to do in Prague is to take the funicular railway ride to admire the view.
Pro Tip: Look out for a miniature version of Eiffel Tower at the summit.
24. Savor a Czech Delicacy, Pickled Cheese
A must-try for a Prague tourist is the Pickled Cheese. Pairs well with a glass of cold pivo, this is a classic pub favourite. Bite into soft Camembert like cheese served hot and creamy with Czech fresh bread. The cheese is made in pickled oil, spices, garlic, and chilli peppers. Eating this delicious snack is a popular thing to do in Prague.
Pro Tip: You could try a food tour if you are a traveller foodie.
25. Sip Czech Beer at the Czech Beer Museum
Enjoy tasting four samples of Czech beer and learn about Czech beer history. An interesting thing to do in Prague is a visit to understand the brewing history at the Czech Beer Museum. You could also bottle your beer and label it at the end of the visit, Learn about the secrets of a good brewing process.
Timings: 12 noon to 8.00 pm
Average Price: $20.16 per person
Pro Tip: You can cancel up to 24 hours in advance and receive a full refund.
26. Enjoy Sightseeing with Dinner in an Open-Top Glass Boat Tour
Enjoy a 3-hour cruise and marvel at the beauty of Prague at night. The historical monuments are illuminated including the Prague Castle. A 3-hour buffet cruise onboard a modern open-top glass boat is a beautiful thing to do in Prague. Mobile ticketing is also provided and booklets in various languages are distributed onboard. Sip a glass of Prosecco served as a welcome drink.
Timings: 7.00 pm
Pro Tip: Enjoy a selection of Czech traditional cuisine at the onboard buffet.
27. A Tandem Skydiving Adventure in Prague
An adventurous thing to do in Prague is to climb to an altitude of 15,000 feet and leap. Take a free fall through the Prague skies like a bird. Cruise back to earth with your heart pumping as fast as it can. The adrenaline rush on this tandem skydive adventure is mind-blowing. The experience comes with basic training before the dive.
Average Price: $199.19
Pro Tip: Pick up your skydive certificate and a complimentary T-shirt. Don't forget to bring along your sports shoes.
28. Watch the Splendid Change of Guards at the Royal Palace
Watch in amazement the joy of the ceremony, not a very serious and warlike display. Soldiers try to keep a straight face as they go through the choreographed arms swinging of rifles, occasionally making mistakes. Watch a quick chat between the guards. This is very unlike the Buckingham Palace Change of Guards where the soldiers keep a poker face. Go ahead and enjoy the fun and joyful thing to do in Prague.
Timings: 11.00 am to 2.00 pm (Wednesday and Saturday)
Pro Tip: The best time to visit is noon where the ceremonial exchange of flags is accompanied with a trumpet call.
29. Visit the Magnificent Powder Tower
The Gothic Powder Tower is a popular Prague place to visit. It was a starting point of the Royal Route to Prague Castle. Go up to the viewing gallery at a height of 44 metres. The tower was completed in 1475 and is one of the most significant Prague attractions. Processions of Czech kings during their coronation entered the Old Town from here.
Timings: 10.00 am to 6.00 pm (November to February) | 10.00 am to 8.00 pm (March, April to September, October)
Average Price: $11.25 (family price)
Pro Tip: The Powder Tower was once used as a gunpowder store.
30. Marvel at the Dancing House building
A popular thing to do in Prague is a visit to the Dancing House, which has an art gallery and rooftop viewing entrance. The art gallery hosts exhibitions of world art and promotes young talented Czech artists, glassmakers, architects, and designers. You can take a guided tour of the Dancing House Gallery and learn about contemporary art. Enjoy fabulous views from the rooftop terrace for astonishing views of the city.
Average Price: $10.08 (gallery tickets)
Pro Tip: Keep the ticket voucher safe that will allow you access to the entrance of the rooftop terrace.
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redpajama_set_name: RedPajamaCommonCrawl


text:
Q: CSS3 animation doesn't work in Safari or IE Here is the pastebin link so you can easily see what's going on http://pastebin.com/jGQk2JJ8 It's probably quite an easy problem to solve.
Thanks!

A: You can always check first the browser support first.
As it seems, safari browser supports in general the CSS3 animation events, but not all versions do as well.
Check here and compare it with your version of safari.

redpajama_set_name: RedPajamaStackExchange


BY RANIA RICHARDSON | Neighbors rallied in a campaign to minimize the highly disruptive noise from Con Edison's overnight work that began last month on W. 33rd St., between Ninth and 10th Aves. Spearheaded by Julia Campanelli, the community initiative culminated in a town hall-style meeting organized by the District Office of City Council Speaker Corey Johnson for May 1, at which representatives from city agencies and other officials met with local residents to discuss their quality of life issue.
Campanelli, an actress and filmmaker, lives in the rear of a building on W. 34th St. that overlooks the work site, and leaves her air conditioner on to drown out the sound. She has measured volume of up to 90 decibels (dB) from previous work in the area using the smartphone app, "dB meter." According to US Department of Health & Human Services, long or repeated exposure to sounds at or above 85 dB can cause temporary or permanent noise-induced hearing loss.
The current complaint is one of many over the years, as the neighborhood is experiencing an astonishing amount of development and the associated aggravations. The Midtown central business region is growing westward with the creation of the Special Hudson Yards District (the result of a comprehensive rezoning in 2005), and this area, from W. 30th to 41st Sts., between Eighth and 11th Aves., is dense with construction.
The most expansive project in the district, Hudson Yards, from Related Companies and Oxford Property Group, is "the largest private real estate development in the history of the United States," according to the company's press materials. Located west of 10th Ave., it is not germane to the issue at hand.
The May 1 gathering to discuss noise abatement included representatives from Con Edison, Community Board 4 (CB4), the NYC Department of Environmental Protection (DEP), the NYC Department of Transportation (DOT), Port Authority (including Lincoln Tunnel), as well as Brookfield Property Partners. The 10th Precinct was invited but did not attend.
At the time, Con Edison was on site daily from 10 p.m. to 6 a.m. — saw-cutting, jackhammering, and digging trenches through the night. Daytime work is prohibited because City Knoll Middle School is on the block. Ironically, the school children who live within earshot of the racket may be going to class sleep deprived.
With the gas work complete, the next phase is to reinforce the electricity grid to "keep the neighborhood energized" according to Con Edison project manager Michael Moccia. The work will also benefit the Manhattan West development from Brookfield (W. 31st to 33rd Sts., btw. Ninth & 10th Aves.) and Moynihan Station, the expansion of Penn Station into the James A. Farley Post Office Building.
Attendees agreed that eliminating weekend work would provide at least some relief. A few days after the May 1 meeting, Con Edison, with a permit approved by the DOT, removed weekend work from the schedule and extended weekday hours to begin at 8 p.m. The original end date of June 15 was moved to July 7, although the remaining work will be revisited and an earlier finish is possible. The goal is to ensure reliable service in the area before the summertime heat.
Matt Green, who serves as Johnson's Deputy Chief of Staff, District Director, facilitated the meeting. In reference to future issues, he said to the local residents, "The first step to take is to call 311 or use the 311 app (nyc.gov/nyc-resources/service/5460/nyc311-mobile-app) if you have a smartphone. We need an official complaint to be lodged in order to track and follow-up with the city agencies." Communication through 311 will often trigger an inspection.
At the suggestion of the attendees, executives from the DEP had a post-meeting discussion with Con Edison regarding noise mitigation measures to be used by their contractors. Most complaints are for "rock chopping," and it is a challenge to reduce the sound. The options include mufflers on jackhammers, blanket material around the equipment to shroud noise, and portable sound barriers wherever and whenever possible.
In addition, CB4 District Manager Jesse Bodine and Johnson's District Office will be organizing a construction coordination meeting for community input on development in the Midtown West area, in the near future.


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\section*{Abstract}

\textit{Pretrained language models (PLMs) have motivated research on what kinds of knowledge these models learn. Fill-in-the-blanks problem (e.g., cloze tests) is a natural approach for gauging such knowledge. BioLAMA generates prompts for biomedical factual knowledge triples and uses the Top-k accuracy metric to evaluate different PLMs' knowledge. However, existing research has shown that such prompt-based knowledge probing methods can only probe a lower bound of knowledge. Many factors like prompt-based probing biases make the LAMA benchmark unreliable and unstable. This problem is more prominent in BioLAMA. The severe long-tailed distribution in vocabulary and large-N-M relation make the performance gap between LAMA and BioLAMA remain notable. To address these, we introduce context variance into the prompt generation and propose a new rank-change-based evaluation metric.  Different from the previous known-unknown evaluation criteria, we propose the concept of "Misunderstand" in LAMA for the first time. Through experiments on 12 PLMs, our context variance prompts and Understand-Confuse-Misunderstand (UCM) metric makes BioLAMA more friendly to large-N-M relations and rare relations. We also conducted a set of control experiments to disentangle "understand" from just "read and copy".}

\section*{Introduction}

Pre-trained language models (PLMs) like BERT \cite{devlin2018bert} have achieved impressive results on few-shot or zero-shot language understanding tasks by pre-training model parameters in a task-agnostic manner and then fine-tuning to transfer the knowledge to specific downstream tasks \cite{brown2020language, yao2020zero, yao2021improving, kwon2022medjex}.
Recently, researchers have become interested in measuring how much factual information PLMs get from pre-training. \cite{petroni2019language} formally defines this task in the LAMA benchmark, which consists of (subject, relation, object) triples and a corresponding human-written template expressing each relation. They show that BERT can predict objects given task-specific prompts.
In the biomedical domain, \cite{sung2021can} releases the Biomedical LAnguage Model Analysis (BioLAMA) probe. They show that biomedical domain specific PLMs like BioBERT \cite{lee2020biobert} can predict objects in biomedical factual knowledge triples given cloze-style prompts—for example, "nasal polyp has symptoms such as [Mask]."

However, existing research has shown that such prompt-based knowledge probing methods can only probe a lower bound of knowledge \cite{petroni2019language, jiang2020can}.
Even though subsequent work has attempted to improve by finding better prompts \cite{shin2020autoprompt, zhong2021factual}, several biases of the prompt-based probing methods (i.e., Prompt Preference Bias, Instance Verbalization Bias, and Sample Disparity Bias) make the evaluation in the LAMA benchmark unreliable \cite{cao2021knowledgeable, cao2022can}.
This problem is more severe in N-M relation with large N and M \cite{petroni2019language} and in rare relations exhibiting long-tailed distribution \cite{meng2021rewire}.
For example, according to Unified Medical Language System (UMLS) \cite{bodenreider2004unified}, asthmatic has 474 symptoms and cough has at least 66 related diseases. And the frequency of cough (the most common symptom of asthmatic) is much more than other rare symptoms.
Since such large N-M relations and rare relations are common in biomedical domain, they make the BioLAMA's performance much lower than LAMA's \cite{cao2021knowledgeable}.

In this paper, we introduce context variance into the prompt generation to mitigate three prompt-based probing biases highlighted by \cite{cao2022can}. For every biomedical factual knowledge triple, we retrieved contexts from two large and representative biomedical resources: MIMIC-III \cite{johnson2016mimic}, which comprises 59,652 electronic health record (EHR) notes, and a randomly sampled subset of PMC \cite{goldberger2000physiobank}, which includes 50k published biomedical articles.
Following \cite{yang2020generating} work, we collect a corpus of 50K outpatient EHR notes from the Local Medical Center, named LMC-EHRs dataset. The biomedical PLMs are always pretrained on PubMed and MIMIC-III, we want to create this third context resource for a more fair comparison since none of these PLMs saw these EHR data before.
For a more robust evaluation, we created three different kinds of synthetic contexts, which were generated based on corresponding real contexts from the above three resources, to largely increase the variance setting.
Subsequently, we propose a new rank-change-based evaluation metric, namely Understand-Confuse-Misunderstand (UCM).  Different from the previous known-unknown evaluation criteria in the previous rank-based metric (Top-k ACC), we propose a new concept of "misunderstand" in LAMA for the first time.
Through extensive experiments on 12 PLMs, our context variance prompts and UCM metric makes BioLAMA more friendly in Large-N-M relations and rare relations. In further experiments, we conducted a set of control experiments to disentangle "understand" from just "copy", and showed that PLMs, especially biomedical domain-specific PLMs show evidence of "understanding" of some knowledge.

    \begin{figure*}[t]
        \centering
        \includegraphics[width=\linewidth]{figures/context_variance_pormpt_generation_and_UCM_metric.jpg}
        \caption{For triple ($S$, relation, $O_t$), \hlc[yellow]{yellow entity} represents $S$, \hlc[orange]{orange entity} represents $O_t$, \textcolor{red}{red entities} represents other entities that are equally correct in the context ($O_{cor}$), and \textcolor{blue}{blue entities} represents wrong entities in the context  ($O_{incor}$). Here, "correct" means that these entities also satisfy the same relation with $S$. We then split the context into different segments according to these entities. Next, we add the context to the prompt segment by segment, and observe that each time a new segment is added, how the ranking of Target Object ($O_t$), Added Object ($O_a$), Correct Object ($O_{cor}$), and Incorrect Object ($O_{incor}$) will change in the "[MASK]" place.}
        \label{UCM_metric}
    \end{figure*}

\section*{Related Work}
\paragraph{Language Models as Knowledge Bases}
\cite{petroni2019language} introduces the LAMA benchmark and begins work on prompt-based factual knowledge probing. They point out that their benchmark provides only a lower-bound estimate of the amount of factual information stored in PLMs, since their manually written prompts may not be optimal for eliciting facts. Therefore, follow-up work focuses on tightening this bound to find more optimal prompts. \cite{jiang2020can, shin2020autoprompt, zhong2021factual, petroni2020context}.
We are also working towards the same goal, but we do it from a different angle. We explore the way of context variance prompting method and re-design its evaluation metrics according to the characteristics of biomedical domain.

Existing work uses Top-k ACC as the evaluation metric, so there are two possibilities for certain knowledge, "Known" (in Top-k) and "Unknown" (not in Top-k).
\citet{yao2022extracting} added EHR notes as context to the prompt, and proposed a rank-change-base method to further analyze the results. They found that the rank-change-base method can also prove that PLMs have some knowledge very well, because PLMs can have completely different behavior on knowledge and noise under their setting. Our work builds on this rank-change-base method. Thanks to the three situations that will occur in "rank-change" (increase, no change, and decrease), PLMs can have three different status for certain knowledge ("Understand", "Confuse", and "Misunderstand"). Among them, "Misunderstand" is important for the biomedical domain, because the potential harm caused by "Misunderstand", such as hallucination and contradiction, is much greater than just "Unknown" or "Confuse".

\paragraph{BioLAMA} In the biomedical domain, \cite{sung2021can} create and release the BioLAMA benchmark following the LAMA setting, which consists of 49K biomedical factual triples. However, the performance gap between LAMA and BioLAMA remains notable because biomedical domain knowledge probing has its unique challenges (including severe long-tailed distribution in vocabulary, multi-token entities, large N-M relations). \cite{meng2021rewire} propose a new method, Contrastive-Probe, to handle the multi-token challenge during encoding answers. In this paper, we try to handle the other two challenges, long-tailed distribution in vocabulary and large N-M relations evaluation.

\paragraph{Biases in Prompt-based Knowledge Probing}
\cite{cao2022can} highlight three critical biases which could impact performance: Prompt Preference Bias (PPB), Instance Verbalization Bias (IVB), and Sample Disparity Bias (SDB).
PPB shows the LAMA performance may be biased by the fitness of a prompt to PLMs' linguistic preference. which means semantically equivalent prompts may lead to different biased evaluation results.
IVB shows the evaluation results are sensitive and inconsistent to the different verbalizations of the same instance (e.g., representing the COVID-19 with the SARS-CoV-2 or Coronavirus disease 2019).
SDB shows the performance difference between different PLMs may due to the sample disparity of their pretraining corpora, rather than their ability divergence.

In this paper, we introduce context variance into the prompt generation to mitigate these three aforementioned biases.

\section*{Methods}
To quantify a model's knowledge, we re-design the BioLAMA evaluation process. Our assumptions are as follows.

\begin{enumerate}[topsep=0.2pt,itemsep=0.2ex,partopsep=.2ex,parsep=0.2ex]
    \item Adding a context containing subject and object and other relevant entities to the prompt can help the BioLAMA task overcome aforementioned challenges and biases.
    \item The previous rank-based metric is not optimal in model stability and reliability. An improved rank-change based metrics, which best captures rank variations between different inputs, can better evaluate PLMs knowledge.
\end{enumerate}

\subsection*{Context Segmentation and Retrieval}
\label{Context Retrieval and segmentation}

Assume we have one relation ($Subject$, relation, $Object_{target}$), or ($S$, relation, $O_{t}$), to be evaluated:

\begin{enumerate}[topsep=0.5pt,itemsep=0.2ex,partopsep=0.2ex,parsep=.20ex]
    \item We retrieve documents including both subject $S$ and target object $O_t$ from certain resources.

    \item We will target all other entities that have the same entity-type of $O_t$ (e.g. nasal discharge's entity type is "symptom"), named entity-pool ($O_{pool}$). According to certain Knowledge Base (KB), there may be other objects also satisfying the same relation with this subject. So we named all those correct entities in $O_{pool}$ as $O_{cor}$ (red entities in the Figure \ref{UCM_metric}: nasal polyp, symptom, shortness of breath.), and the rest of entities in the $O_{pool}$ as $O_{incor}$ (bleu entities in the Figure \ref{UCM_metric}: nasal polyp, not symptom, wheeze)

    \item We take the $O_t$ as the center and every time look for the next entity in left and right order. We use these entities as delimiters to split the document into segments and ensure that only one entity per segment belongs to $O_{pool}$. These segments are named S1, S2, S3, S4, etc. For example, in Figure \ref{UCM_metric}, the text surrounding the "nasal discharge" is used as S1 until the "sick feeling" is encountered on the left side of S1, and the "postnasal drip" is encountered on the right side of S1, so we ensure that S1 only contains one related entity "nasal discharge". Similarly, S2 adds to the left text of "sick feeling" in the corresponding segment until it encounters "heartburn". S3 adds to the right text of the "postnasal drip" in the corresponding segment until it encounters the "nasal stuffiness". Note that if we can't find a new entity in one side, then we will always search the other side.

\end{enumerate}

\subsection*{Context Variance Prompt Generation}
\label{Context Variance Prompt Generation}
We have no restrictions on the prompt generation methods, both the manual prompt and the trained prompt can be combined with our methods for the probing task. Following the BioLAMA work, for certain triple ($S$, relation, $O_t$), we manually generate the corresponding prompts for probing.

\subsection*{Real Context Prompt}
\label{Real Context Prompt}

In the previous section, we get the required context and split them into several segments. Here, we add these segments to the prompt input in turn and get some new inputs. In order to keep the structure of the context, we will put every segment in the correct position (the same with the original text) when adding a new segment. For example, in Figure \ref{UCM_metric}, we can use the manual prompt method to generate a prompt like "nasal polyp has symptoms such as [Mask].", which is the input0. We put S1 before the prompt and get input1. Because S2 is on the left side of S1 in the original text, so we put S2 before S1 and get input2. Because S3 is on the right side of S1 in the original text, we put S3 after S1 (before the prompt) and get input3, and so on.

    \begin{figure*}[t]
        \centering
        \includegraphics[width=\linewidth]{figures/synthetic_context_and_negativeE_generation.jpg}
        \caption{The three Synthetic Contexts and Negative-target Real context are mentioned in the Method section. Assume we already have one Real context, we remove all non-knowledge content of the Real Context to generate the Knowledge-only context. We continue to concatenate S1, S2, S3 ... in the order from left to right to generate the knowledge-sorted context. We randomly shuffle all segments in the Knowledge-only context and then rename S1 to S10 with $O_t$ as the center to generate the Knowledge-random context. For Negative-target Real context, we find the closest $O_{incor}$ to $O_t$ in the Real context and do the segmentation again using this $O_{incor}$ as the new center (the original $O_t$ is only an $O_{cor}$ in the context).}
        \label{context_variance_and_negativeE}
    \end{figure*}

\subsection*{Synthetic Context Prompt}
\label{Synthetic Context Prompt}
We call the contexts described in the previous section as real contexts, because they are retrieved texts from real-world resources.
However, there are not only knowledge (entities) in the real context, but also other texts. The performance of PLMs in prompt-based probing tasks is not only determined by knowledge, but also affected by language ability in the whole context. In order to measure knowledge more accurately, and also to add more variance, we design three synthetic context generation methods. In Figure \ref{context_variance_and_negativeE}, we continue with the previous example to describe how we generate these synthetic contexts.

\paragraph{Knowledge-only context} For the real context, we remove those non-knowledge texts in each segment, and only keep knowledge in the context. The positions of all segments remain unchanged. The synthetic context generated in this way simply removes the "non-knowledge" content.

\paragraph{Knowledge-sorted context} We sort the segments in ascending order for the Knowledge-only context, and add them to the synthetic context from left to right (S1 S2 S3 ... + prompt). The synthetic context generated here not only removes the "non-knowledge" content, but also changes the relative position between different segments in the input. However, the order in which each segment is added to the input remains unchanged.

\paragraph{Knowledge-random context} We keep S1 in the first because it contains the target object. Then we randomly pick the next segment from the remaining segments and add it to any position in the input. The synthetic context generated in this way only preserves the same object-pool. But the order and position of the segments are added at random.

\subsection*{N-M Relations and Rare Relations Friendly}

The performance gap between LAMA and BioLAMA remains notable because biomedical domain knowledge probing has its unique challenges, and as stated previously, two of them are severe long-tailed distribution in vocabulary (rare relation unfriendly) and many large-N-M relations \cite{sung2021can, meng2021rewire}. Because the rare relation rarely appears in pretraining data, it is difficult for prompts to elicit these facts. The reason behind this is that PLMs are context sensitive, and only specific prompts as contexts allow them to generate corresponding results. For common relation, they are frequently mentioned in different contexts in pretraining, so the requirements for prompt are relatively broad. In contrast, even if a PLM knows a rare relation, it has very strict requirements on the prompt, that is, the prompt used must be very related to the context in these PLMs' pretraining step. On the other hand, the large-N-M relations means the prompt has lots of objects that may be the right answers, however, the model cannot distinguish the difference between $O_t$ and other $O_{cor}$. Due to the softmax bottleneck of PLMs \cite{yang2017breaking, chang2022softmax}, if the model prefers to put other $O_{cor}$ on Top-k, it may not be able to put $O_t$ on Top-k at the same time. Therefore, the previous evaluation is unfair and inaccurate.

In this paper, we introduce context variance in the prompt generation process to alleviate the above two problems. Because of the attention mechanism, PLMs often "rely too much on copying from the context" \cite{li2019don, petroni2020context}, which means PLMs usually directly copy other entities mentioned in the context. This is sometimes a big weakness of PLMs for generational tasks, but it is unexpectedly well suited for our use case. If we add the context containing a specific set of $O_{pool}$ to the prompt as the input, PLMs will naturally have candidates (i.e. $O_{pool}$) at the [MASK] position because of attention mechanism. As long as candidates contain $O_t$, no matter whether ($S$, relation, $O_t$) is rare or common relations, or 1-1 or N-M relations, we can use the unified method to observe different behaviors when PLMs treat different inputs. We define a set of procedures in the next few sections to determine whether PLMs know this knowledge according to different behaviors.

\subsection*{Negative-target Context}
\label{Negative-target Context}
Because of the attention mechanism, PLMs often "rely too much on copying from the context" \cite{li2019don, petroni2020context}. Specific to behavior, if PLMs do not understand certain knowledge, they may directly copy non-relevant or incorrect entities mentioned in the context.
Conversely, if PLMs have certain knowledge, they may be less likely to do so.
In other words, in BioLAMA, the knowledgeable PLMs will rank higher
to the correct than incorrect objects in the [MASK] position.
In order to verify this idea, we designed the corresponding Negative-target context for each context mentioned. As shown in Figure \ref{context_variance_and_negativeE}, we search for $O_{incor}$ from the segments in the context in the order of S1, S2, S3, S4.... We take the first $O_{incor}$ found as the Negative-target of this context, and treat the original $O_t$ as a normal $O_{cor}$. Then we re-construct the context-variance prompt belonging to the negative-target by using all our methods described above with the negative-target as the center. Therefore, for each $O_t$-centered normal data, we will have a corresponding negative-target-centered comparison data. We hope to use such control experiments to explore whether PLMs behave completely different for the two data. We use this as a distinction between "read from context and copy the knowledge" and "really understand the knowledge". The relevant results are presented in table \ref{UCM_m}.

\subsection*{Rank-change Calculation}
\label{Rank-change Calculation}
As described in the previous section, because of the attention mechanism, PLMs give entities that appear in the context more likely to appear at the [MASK]. Therefore, if we continue to use Top-k ACC, $O_t$ will always have a particularly high probability of being judged as "known", because all the contexts we add contain $O_t$. Since the rank-based metric is not suitable for evaluating the context variance prompt probing task, we design a rank-change-based metric.

As shown in Figure \ref{UCM_metric}, when we have continuous segments S1, S2, S3, S4...., we regard the context as a process of continuously adding new segments centered on $O_t$. In this process, each newly added segment will only contain one entity, named $O_a$. Here, $O_{a}$ may belong to $O_{cor}$, and may belong to $O_{incor}$. Intuitively, PLMs with the corresponding knowledge should have different behaviors for these two different situations. In particular, we try to understand how PLMs behave differently for different entities in context by observing the following four sets of Rank Change (RC) results. As shown in Figure \ref{UCM_metric}, assuming that we add S5 to input4:

\begin{enumerate}[topsep=0.2pt,itemsep=0.2ex,partopsep=.2ex,parsep=0.2ex]
    \item RC-$O_t$: change in the ranking of $O_t$
    \item RC-$O_a$: change in the ranking of $O_a$ in S5
    \item RC-$O_{cor}$: average change in the ranking of all existing $O_{cor}$ in the output4
    \item RC-$O_{incor}$: average change in the ranking of all existing $O_{incor}$ in the output4
\end{enumerate}

Here, the "change in the ranking" refers to the ranking of the corresponding entity in output5 minus its ranking in output4. The result of RC (output5-output4) will have three cases, ranking-increasing (negative), ranking-unchanged (zero) and ranking-decreasing (positive). It is interesting to observe how PLM's RC behaves differently when a new $O_a$ come ($O_a$ can be either $O_{cor}$ or $O_{incor}$). We put this results in table \ref{behavior}.

\subsection*{Understand-Confuse-Misunderstand Metric (UCM)}
\label{Understand-Confuse-Misunderstand Metric (UCM)}

The BioLAMA tries to verify whether or not a certain PLM has knowledge about ($S$, relation, $O_t$), so in the four groups of RC scores, we can directly take RC-$O_t$ as the metric.
So, according to the results of Rank-change, we define the UCM metric as follows. Suppose we get a series of RC scores by the method described:

\begin{enumerate}[topsep=0.2pt,itemsep=0.2ex,partopsep=.2ex,parsep=0.2ex]
    \item For each input, we check whether $O_a$ belongs to $O_{cor}$ and leaving only those RC for which $O_a$ belongs to $O_{cor}$

    \item For the remaining data, we count their distribution, and take the proportion of RC-$O_t$ less than 0 as the Understand score, the proportion of RC-$O_t$ equal to 0 as the Confuse score, and the proportion of RC-$O_t$ greater than 0 as the Misunderstand score
\end{enumerate}

We name the above steps as knowledge-level-UCM ($UCM_k$), which is consistent with the purpose of previous metric Top-k ACC. $UCM_k$ tries to verify whether a certain PLM knows a certain triple (knowledge) in one relation (like disease-symptom relation), and then calculate the respective percentages of U, C, and M for all triples in this relation for evaluation. The previous metric Top-k ACC is doing the same thing, except they compute the percentages of known (in Top-k) and unknown (not in Top-k).

Similarly, if we not only want to verify whether a certain PLM has a certain knowledge, but directly compare whether some PLMs are more knowledgeable, then we can collect all context variance prompts for all relations' triples in a certain KB, and use the same steps above to get the RC distribution over this KB. With this we can compare the UCM scores of different PLMs to tell which PLMs are more knowledgeable. We name it as model-level-UCM ($UCM_m$). We put the results of $UCM_k$ in table \ref{UCM_k_ACC_wo_context_comparasion}, and the results of $UCM_m$ in table \ref{UCM_m}.

\subsection*{Mitigating PPB, IVB, and SDB Biases}
\cite{cao2022can} highlights three critical biases which affect the correctness and fairness of evaluating different PLMs on the BioLAMA benchmark. We evaluate whether context variance and UCM can alleviate the three biases.

\paragraph{Prompt Preference Bias (PPB)}
By adding context variance, although the prompt itself does not change, the input to the PLMs is not the same. Because UCM is a statistical result of a large number of context variance prompts, only those PLMs that can be regarded as "Understand" in most data are treated as "really know" the corresponding knowledge. Therefore, UCM will not encounter serious PPB like the previous method because only one prompt is used for probing all PLMs.

\paragraph{Instance Verbalization Bias (IVB)}
In our methods, we first map text to the UMLS concepts and therefore eliminate the verbalization challenge at the method level. At the same time, the context variance prompts contain a large amount of variations from real-world resources, so different verbalization can be used as input to PLMs. These all help us relieve IVB.

\paragraph{Sample Disparity Bias (SDB)}
We control SDB by choosing resources more fairly. All biomedical domain specific PLMs selected in this paper are pre-trained on the representative biomedical text resources: PubMed or MIMIC-III. We also use LMC-EHRs as a third resource, and no PLMs in our evaluation have seen this dataset.

\section*{Results}
\subsection*{Experimental Setup}

We conduct the experiments on 12 PLMs, including BERT (base and large) \cite{devlin2018bert}, RoBERTa (base and large) \cite{liu2019roberta}, BioBERT \cite{ lee2020biobert}, ClinicalBERT \cite{alsentzer2019publicly}, 3 kinds of BioLMs \cite{lewis-etal-2020-pretrained}, and 3 kinds of BlueBERTs \cite{peng2019transfer}. All the results in the Experiments part are the average scores of three different resource data as context.
We use manual prompts created by domain experts following BioLAMA. It is important to note that we have no restrictions on the prompt generation methods, both the manual prompt and the trained prompt can be combined with our methods.
We use a fill-in-the-blank cloze statement (i.e., a "prompt") for probing. Since the majority of entities in our dataset are made up of multiple tokens, we also implement a multi-token decoding strategy following \cite{sung2021can}.
We mainly use UCM introduced our Method section as the evaluation metric, but we also report the results of using the previous metrics following BioLAMA with Top-k accuracy, which is 1 if any of the Top k object entities are included in the annotated object list, and is 0 otherwise.

\subsection*{Rank-change}
Table \ref{behavior} shows PLMs' rank-change behaviors. Generally speaking, because $O_a$ is added to the context, there will be a ranking-increase because of the attention mechanism. $O_t$ belongs to $O_{cor}$, so intuitively there will be the same trend of RC. And $O_{cor}$ and $O_{incor}$ should have the completely opposite trends. Depending on the difference of $O_a$ in the newly added segment ($O_a$ may belong to either $O_{cor}$ or $O_{incor}$), the specific changes of the four groups of RC will also be different. We find that if $O_a$ belongs to $O_{cor}$, then $O_{cor}$ and $O_t$ should have the same trend of change (rank increase) as $O_a$, otherwise $O_{incor}$ will have the same trend of change as $O_a$.

    \begin{table}
        \centering
        \begin{tabular}{l|cccc|cccc}
        \hline

        & \multicolumn{4}{c|}{$O_a \in O_{cor}$} & \multicolumn{4}{c}{$O_a \in O_{incor}$} \\

        & $O_t$ & $O_a$ & $O_{cor}$ & $O_{incor}$ & $O_t$ & $O_a$ & $O_{cor}$ & $O_{incor}$\\

        \hline
        BERT & -0.98 & -6.56 & -2.8 & 0.045 & 0.78 & -1.98 & 0.74 & -0.59\\

        BioBERT & -2.07 & -12.7 & -5.62 & 0.23 & 1.04 & -6.05 & 1.06 & -2.02\\

        BioLM & -1.59 & -10.56 & -5.2 & 0.09 & 0.88 & -3.76 & 0.91 & -1.32\\

        \hline
        \end{tabular}

        \caption{With different contexts, rank-change measures changes of predictions of [MASK] in prompts by different PLMs. There are three cases for rank change, ranking-increasing (negative), ranking-unchanged (zero) and ranking-decreasing (positive).
}
        \label{behavior}
    \end{table}

\subsection*{$UCM_K$ and Top-k ACC}
\label{Compare UCM_K and Top-k ACC}

Here we show that UCM can be a more appropriate metric than Top-k ACC. We compare $UCM_k$ with top-k accuracy with or without context.Table \ref{UCM_k_ACC_wo_context_comparasion} shows that evaluation results with Top-k ACC is often counter-intuitive and unstable. For example, PLMs pretrained on the biomedical domain corpus (e.g., clinicalBERT) get much lower scores than the general domain PLMs (e.g., BERT). The clinicalBERT and BioBERT are trained in the biomedical domain and have similar performance in most of the downstream tasks, yet they show a very huge gap in their performance.
Table \ref{UCM_k_ACC_wo_context_comparasion} shows that when context is included, almost all BioLMs' ACC@5 have non-distinguishable ~90\% scores,
and we cannot distinguish whether the model is 'copy' or understand based on the evaluation with Top-k ACC.

In contrast, the results with $UCM_k$ are more interpretable and consistent. For both BERT families and BioLMs families in table \ref{UCM_k_ACC_wo_context_comparasion},
the results show that large models have better scores (higher U, lower or similar C and M) than the corresponding base models. We additionally analyze the scores for each resource. We find that when we use LMC-EHRs data as the context, biomedical PLMs (BioBERT, clinicalBERT, RoBERTa-base-PM, RoBERTa-base-PM-M3) all have better performance (higher U, lower C and M) than the general domain PLMs (BERT-base and RoBERTa-base). Since MIMIC-III is an EHR resource and in constrast PMC comprises biomedical literature articles, PLMs pretrained on MIMIC-III have a better performance (similar U and C, lower M) on LMC-EHRs data. All these results illustrates that introduction of "Misunderstand" makes the results more consistent and interpretable. Although PLMs trained on both PMC and MIMIC-III have knowledge inside LMC-EHRs context, a more similar domain (MIMIC-III) can provide more accurate knowledge, making these PLMs less "Misunderstand".

    \begin{table}
        \centering
        \begin{tabular}{l|cc|cc|ccc}
        \hline

        & \multicolumn{2}{c|}{without context} & \multicolumn{5}{c}{with context} \\

        \hline

        &  \small{acc@1} &  \small{acc@5} & \small{acc@1} &  \small{acc@5} & U & C & M\\

        \hline
        \small{BERT-base} & \small{0.0038} & \small{0.57} & \small{0.327} & \small{0.68} & \small{0.119} & \small{0.787} & \small{0.094} \\

        \small{BioBERT} & \small{0.227} & \small{0.639} & \small{0.407} & \small{0.881} & \small{0.328} & \small{0.427} & \small{0.243} \\

        \small{ClinicalBERT} & \small{0.014} & \small{0.264} & \small{0.391} & \small{0.812} &  \small{0.302} & \small{0.533} & \small{0.165} \\

        \small{BERT-large} & \small{0} & \small{0.009} & \small{0.258} & \small{0.665} & \small{0.158} & \small{0.738} & \small{0.103}\\

        \hline

        \small{RoBERTa-base} & \small{0.11} & \small{0.218} & \small{0.572} & \small{0.872} & \small{0.287} & \small{0.495} & \small{0.217}\\

        \small{RoBERTa-base-PM} & \small{0.134} & \small{0.281} & \small{0.412} & \small{0.902} & \small{0.298} & \small{0.427} & \small{0.274}\\

        \small{RoBERTa-base-PM-M3} & \small{0.102} & \small{0.273} & \small{0.563} & \small{0.904} & \small{0.299} & \small{0.432} & \small{0.267}\\

        \hline

        \small{RoBERTa-large} & \small{0.107} & \small{0.229} & \small{0.503} & \small{0.865} & \small{0.304} & \small{0.509} & \small{0.186}\\

        \small{RoBERTa-large-PM-M3} & \small{0.105} & \small{0.31} & \small{0.644} & \small{0.915} & \small{0.312} & \small{0.432} & \small{0.255}\\

        \hline
        \end{tabular}
        \caption{Top-k ACC VS $UCM_k$}
        \label{UCM_k_ACC_wo_context_comparasion}
    \end{table}

\subsection*{Evaluation with UCM}
\label{Evaluation_UCM}

We compute $UCM_k$ and $UCM_m$ to compare the knowledge level between different PLMs. We want to note that even though we show that UCM is a more reliable metric than Top-k ACC, it is not easy to fairly compare the knowledge level between different PLMs. Too many factors may affect the knowledge learned by the PLMs during pretraining, so we have to choose the more easily interpretable models to compare. For example, in table \ref{UCM_k_ACC_wo_context_comparasion} and \ref{UCM_m}, RoBERTa-base is a general-domain PLM, RoBERTa-base-PM continues to train RoBERTa-base on PubMed, and RoBERTa-base-PM-M3 continues to train RoBERTa-base-PM on MIMIC-III. So the comparison between this group of PLMs is more meaningful, because the results in table \ref{UCM_k_ACC_wo_context_comparasion} and \ref{UCM_m} can tell us that if the PLMs continue to train on the target-domain related data, their knowledge level will be higher.

    \begin{table}
        \centering
        \begin{tabular}{l|ccc|ccc}
        \hline

        & \multicolumn{3}{c|}{target} & \multicolumn{3}{c}{negative-target} \\

        & U & C & M & U & C & M\\

        \hline
        \small{BERT-base} & \small{0.205} & \small{0.73} & \small{0.060} & \small{0.026} & \small{0.92} & \small{0.049}\\

        \small{BioBERT} & \small{0.32} & \small{0.584} & \small{0.095} & \small{0.103} & \small{0.72} & \small{0.175}\\

        \small{ClinicalBERT} & \small{0.37} & \small{0.56} & \small{0.061} & \small{0.07} & \small{0.79} & \small{0.134}\\

        \small{RoBERTa-base} & \small{0.23} & \small{0.67} & \small{0.099} & \small{0.055} & \small{0.83} & \small{0.11}\\

        \small{RoBERTa} & \small{0.301} & \small{0.583} & \small{0.115} & \small{0.109} & \small{0.72} & \small{0.168}\\

        \small{RoBERTa-base-PM-M3} & \small{0.335} & \small{0.55} & \small{0.112} & \small{0.107} & \small{0.77} & \small{0.12}\\

        \small{BlueBERT-base-PM} & \small{0.383} & \small{0.554} & \small{0.062} & \small{0.026} & \small{0.89} & \small{0.085}\\

        \small{BlueBERT-base-PM-M3} & \small{0.407} & \small{0.559} & \small{0.033} & \small{0.025} & \small{0.89} & \small{0.078}\\

        \hline

        \small{BERT-large} & \small{0.199} & \small{0.75} & \small{0.045} & \small{0.026} & \small{0.9} & \small{0.047}\\

        \small{RoBERTa-large} & \small{0.325} & \small{0.614} & \small{0.061} & \small{0.047} & \small{0.86} & \small{0.113}\\

        \small{RoBERTa-large-PM-M3} & \small{0.346} & \small{0.556} & \small{0.097} & \small{0.063} & \small{0.82} & \small{0.113}\\

        \small{BlueBERT-large-PM-M3} & \small{0.388} & \small{0.504} & \small{0.108} & \small{0.053} & \small{0.85} & \small{0.095}\\

        \hline
        \end{tabular}
        \caption{PLMs' $UCM_m$ scores of $O_t$ as center or negative-target as center}
        \label{UCM_m}
    \end{table}

According to table \ref{UCM_m}, We have the following findings based on the $UCM_m$ scores. We re-emphasize that although the findings we concluded from the $UCM_m$ results are not particularly surprising, such "reasonable" and "intuitive" results can not be obtained with previous metrics. So we make up for these evidences for the biomedical domain with a more reasonable BioLAMA metric.

\begin{enumerate}[topsep=0pt,itemsep=0ex,partopsep=0ex,parsep=0ex]
    \item Large models are always better

    \item The similarity between the context domain and PLMs' pretraining domain is decisive

    \item Training longer does not make PLMs more knowledgeable, which is in line with recent work \cite{goyal2021training}

    \item Real and Knowledge-only contexts do not have much differences.

    For three synthetic contexts, Knowledge-only context and Knowledge-sorted context do not have big differences, but the Knowledge-random context results are unstable.

\end{enumerate}

\section*{Discussion}

\subsection*{Copy or Understand}

The attention mechanism makes PLMs tend to copy knowledge entities in context. The Top-k ACC results with and without context in table \ref{UCM_k_ACC_wo_context_comparasion} also prove that this phenomenon does exist in our task setting. So, after adding knowledge into context prompts, there is a possibility that PLMs copy from context without understanding. To distinguish the two, we introduce the negative-target context in the Method section. In table \ref{UCM_m}, by comparing PLMs' $UCM_m$ score $O_t$ as center or instead negative-target as center, we find that the behaviors of PLMs for the two are almost completely opposite. This control experiment suggests that PLMs understand these knowledge, and do not just copy.

\subsection*{Conclusion}

In this paper, we show that adding context containing relevant entities to the prompt can mitigate two unique challenges (long-tailed distribution in vocabulary and many large-N-M relations) and three biases (PPB, IVB, and SDB) in the BioLAMA task. Then we find the previous rank-based metric is not optimal in model stability and reliability. An improved rank-change-based metric, which best captures rank variations between different inputs, can better evaluate PLMs' knowledge.

\makeatletter
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NC State Day of Giving!
NC State has always been a place that thinks big and gets things done. Because of you, our first-ever Day of Giving was no exception. Thank you for coming together to support the future of the Pack.
Your gifts go further on NC State Day of Giving!
Chancellor Woodson set aside $145,000 in separate bonus funds to be awarded to leaders in overall donations and in donor participation for the Red and White Divisions, as well as for a special university initiatives challenge.
Congratulations to all NC State Day of Giving challenge winners and a huge thanks to those who helped each fund earn them!


How to choose a peristaltic pump-Peristaltic pump,dispensing peristaltic pump,programmable peristaltic pump manufacturer - Chuangrui precision pump Co., Ltd.
CR peristaltic pump is a type of positive displacement pump used for pumping a variety of fluids. If we have a need, how do we choose a peristaltic pump? Let me crpump tell you about it.
1. All peristaltic pumps flow data are all tested in clear water and the medium will have impact on flow rate. For high viscosity liquid, high solid-content of liquid, poor fluidity of liquid, the actual flow and marked flow will have a great decay!
2. the flow parameters are tested in the water under the condition of micro head lift and suction. If suction is too long, the flow rate will decline and the flow loss of head lift is small.
3.the required flow rate should be within the limit flow. In the actual selection, especially for long-term-operation pump, we strongly recommend that the pump at low speed can meet the flow requirements (≤ 300rpm). Long-term high-speed operation, will reduce the pump life, and wear out the tubing soon, which is consumable and expensive.
Second.confirm complete peristaltic pump or OEM.
1.the complete pump, a standardized product , with a set of complete function, will run when access to power supply; OEMs are generally processing products, or accessories, so users need to have the appropriate expertise, installed OEMs on their own equipment to run equipment.
2.the complete pump, a standardized product , with a set of complete function, will run when access to power supply; OEMs are generally processing products, or accessories, so users need to have the appropriate expertise, installed OEMs on their own equipment to run equipment.
For OEMs you need to consider more factors, such as price, flow rate, functions, motor, tubing, power supply and so on. We provide a variety of power levels, generally divided into alternating current and DC, in which AC is divided into single-phase civilian power and three-phase power and DC is generally divided into 6/12 / 24V DC. The DC motor will run when accesses to the corresponding DC power supply. Furthermore, positive and negative only have impact on the motor direction and Although the direction is wrong, the pump will not be burned.
Stepper motor and brushless motor in the peristaltic pump is very widely used, and they must access to the corresponding drive to run. Ordinary DC motor can adjust speed within a narrow range, and stepper motor can adjust speed within a wide range. Please note the circuit board will be burned when negative and positive is wrong.
Pump heads are core components, you have to select it according to the requirements. They are generally divided into quick loading, easy loading and spring type pump head. Quick loading type is generally used for OEM products, easy loading type is widely used in the complete pump; Spring type have some applications in the OEM and the complete pump. Each pump head has its limit flow rate, and each pump head is only suitable for loading one wall thickness tubing.
Peristaltic pump tubing is a professional tubing. And its size, material, resilience, hardness design all have very strict technical requirements!
1.Please select pump tubing according to chemical liquid of pumped liquid, especially corrosive liquid. There are mainly domestic or imported tubing in the market, in which the domestic tubing is made of silicone rubber and can resist chemical corrosion and for more requirement, users have to rely on imported tubing. According to the chemical quality, the users can select the tubing material. For details, please refer to Pump Tubing Resistance Chemical Compatibility Table. And our product engineers will provide accurate recommendation on the base of your chemical component.
2.The general domestic silicone life is usually more than 500 hours (300rpm, medium; lean water, room temperature), the life of imported tubing such as long-life tubing, can be up to 10,000 hours.


text:
Meetinghouse
Joao Antonio Nsesikilandamoko, Portland: Don't look back
My adventure starts in July 2016 when for security reasons, my wife and I decided to abandon everything we were able to build in order to protect and save our offspring.
In Angola I was living perfectly good. Despite the insecurity we were facing, life was treating us well because we had the minimum to call our own. My experience is that in life it is not easy to build something strong, have a good education, have your own family and a chance to be successful in the continent like Africa. So when you are forced to leave everything behind and seek for another opportunity is always a subject of discordance within the family.
Joao Antonio Nsesikilandamoko
Joao Antonio Nsesikilandamoko of Portland is an asylum seeker from Angola. He is enrolled in the high school diploma program at Portland Adult Education.
Immigrants hope for a better future of our kids. Here, our kids receive a good education. In Africa, is not always the same by comparison. Here kids are protected medically and politically. Medically, because they receive medical assistance and the health of kids is a bigger priority here than in Africa. Politically, because the U.S. has established laws protecting kids, unlike in our country where the law can exist but it is not enforced.
Immigrants, when they first arrive in the United States, are confronted with cultural aspects such as learning a new language, applying for asylum, finding a decent place to live and even being forced to beg for food, clothes, etc. This scenario comes as a shock to most of us. People with higher education degrees are doing jobs such as housekeeping because our diplomas or degrees are not considered. The immigration process is complicated. Older people arriving here are seeing their chances to succeed reduced. Immigrants live with the fear of being refused asylum.
"Starting over" destabilizes some of the marriages because from the African perspective, the marriage is not the same as from the American perspective. Here, women are more independent.
Starting over is an adventure whose ending no one can perfectly imagine. In starting over, you must decide to forget that you have already existed. You have to adopt the American system and make yourself like you just been born in America, so it will help you to be familiar with all the systems and cultures.
Starting over finally is like Jesus on the cross when he said, "Father, in your hands, I put my spirit …" We are trying to forget what we have already accomplished in our lives because if we do not want to suffer more, we have to start with a blank slate.
We always remember the saying "whoever puts his hand on a plow does not look back." We believe that the end will be happy.
Joan Perry, Falmouth: Move comes with good omens
Kathleen Sullivan, Freeport: The end of the road is a new beginning
Past Meetinghouse Topics
Instructions - December 2022
Games - November 2022
Decisions - October 2022
Borrowed - September 2022
Up all night - August 2022
First Impressions - July 2022
Broke - June 2022
Guests - May 2022
Grit - April 2022
Mischief - March 2022
Quitting - February 2022
New Kid - January 2022
Overheard - December 2021
Ghosts - November 2021
Old Friends - October 2021
Hometown - September 2021
Moving Day - August 2021
Keepsakes - July 2021
Mistakes - June 2021
Siblings - May 2021
Gardens - April 2021
Haircut - March 2021
Rural Life - February 2021
Silver Linings - January 2021
Surprise - December 2020
Reunion - November 2020
The Attic - October 2020
Locked Out - September 2020
After The Storm - August 2020
Inheritance - July 2020
In The Driver's Seat - June 2020
The Last Time - May 2020
Forgive Me - April 2020
A Fish Out of Water - March 2020
Let's Go - February 2020
Ask Your Mother - January 2020
How to submit to:
On the first Sunday of each month and every Thursday for the rest of that month, we will publish stories by readers on the following topics:
"Maps" – deadline for submissions Dec. 30.
"Style" – deadline for submissions Jan. 27.
"Bikes" – deadline for submissions Feb. 24.
Submissions should be roughly 200 to 500 words in length. Please include your name, hometown and a daytime phone number for verification. Also, give us a one-sentence description of who you are, and a digital photo of yourself or something that speaks to the theme of your piece.
Stories can be emailed to [email protected]; or mailed to Meetinghouse, Portland Press Herald, P.O. Box 1460, Portland, ME 04104-5009 (mailed photos should include a return envelope).


Counseling individuals with a developmental disability – e.g., autism, cerebral palsy, brain injury, and Down syndrome – is honestly tough. Your clients are mostly kids who acquired the neurological disorder from birth or several years after that. They have not lived long enough to identify how to think and behave like regular people. It may even be easier to get through to depressed or addicted folks as they at least lived a life without such a condition in the past.
It does not help either that most – if not all – developmental disabilities are incurable. It's hard to decide if that is because the problem is too complex to resolve or there's only a little research being done on the subject matter. The outcome is that the children who have the disorders mentioned above usually need to stay on therapy for years.
Nonetheless, in case you are new at giving counseling to someone with a developmental disability, check out the following tips.
The unfortunate truth about disabled individuals who are older than five years old is that they may have been exposed to the world. That cannot produce a positive impact on the person all the time because they get to deal with the harsh comments from other people, no matter how much their family tries to protect them. Some gaze at them pitifully; others are inconsiderate enough to talk about their flaws openly.
The thing is, whether the disability altered someone's brain function or not, he or she can still realize at one point that strangers see them differently. It may enable them to retreat under their shell and not want to even be around mental health professionals like you. Because of that, you ought to earn their trust first.
The easiest way to do that is by not pushing the client to do the activities you laid out. It will not be the only meeting you'll have, and perhaps he or she can become comfortable around you the more you stay in their life. You may also learn about their favorites so that it can be like a conversation starter between the two of you.
When someone has a developmental disability, it entails that he or she has problems using his or her senses or body parts regularly. For instance, the twinkling lights that look fun for non-disabled people are triggering for folks with autism. Walking is practically impossible for cerebral palsy patients. Others have issues with their speech, behavior, and deduction process.
Even if the individual refuses or cannot speak, an excellent counselor should be able to understand the client by assessing their body language. Say, in case a patient who has never looked up when you tell a story suddenly meets your eyes, that is a sign that you got their attention. Once a child with special needs tries the activities you suggest to them, it means that the treatment is slowly but surely working.
You can obtain better results as well if you make every activity or discussion relatable for your clients. However, you need to ensure that the technique you will employ is custom-fit to each person's needs.
Remember that you will never get two people who have the same case, despite their condition being under one category. They are different when it comes to the severity of the disability, their age, the trigger factors, and many more. So it is only proper to plan how the treatment should go for a specific client after a couple of sessions, not right after the first consultation.
When you give counseling to someone with a developmental disability, give it with confidence. The guidance you can offer to clients is essential for improving their lives and their future. It is impossible not to know how to do that, primarily now that you have these basic tips.
Marie Miguel has been a writing and research expert for nearly a decade; covering a variety of health-related topics. Currently, she is contributing to the expansion and growth of a free online mental health resource with BetterHelp.com/advice. With an interest and dedication to addressing stigmas associated with mental health, she continues to target subjects related to anxiety and depression specifically.
As an editor, contributor, and writer for over 100 online publications Marie has covered topics related to depression, anxiety, stress, grief, various phobias, and difficult family circumstances. With regular content published on mental health authorities like TheMighty, Yahoo, GoodMenProject, ADAA, CCPA-ACCP, Silverts, AMHCA, etc... Marie has shown both her passion and dedication to discussing & educating topics related to mental health and wellness.
With an understanding that there is never too much information and helpful research about mental health in all of its forms, she continues to look for new and creative ways to both start discussions & engage with others about these important topics.
Before becoming an online researcher and writer, she worked as an Administrative Executive with different industries namely telecom, security workforce providers, trading companies, exclusive hotel and concierge services. After ten years of working in different industries, she decided to enter the world of freelancing in able to give more time to her precious daughter. Given this opportunity, it helped her discover and realize that she is both capable and passionate about expressing her opinions in creative and influential ways via writing.
Marie Miguel is a loyalty awardee of St. Paul College where she spent her primary and secondary education. She holds a degree of Bachelor of Science in Business Administration major in Computer Applications from De La Salle University - College of St. Benilde where she was also on the Dean's List for consecutive semesters during her college years.
"My Philosophy on Mental Health & Wellness"
It takes passion for being an expert researcher and writer of mental health related topics. Having lived through traumatic experiences in the past, it has become easier to express my opinions and findings I've discovered while researching a variety of situations and subjects. I aim to inspire every person that reads mental health & wellness related articles to provide hope in every struggle; just as my experiences have taught me. Additionally, I strive to contribute to the continual progression of mental health awareness by providing helpful information and significant resources to understand further the importance of keeping a healthy mind and well-being.


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No, this isn't the work of a mad 50s scientist, Predicta is a modern company making TVs heavily influenced by mid-century design.
Check out the Predicta website for the full range of weird and wonderful TVs and more details on our favourite model, the Predicta Meteor. Designed by Mike L. Scott, the Meteor has aircraft-inspired legs, a hand-built hardwood cabinet and a 24-inch screen, which pivots on a polished brass collar.
And although these TVs are designed to fit into period houses, they're still packed with the latest technology – easily connecting up with your cable/satellite supplier and video/DVD player.
Now the bad news – well for some of us. These are only currently available in the US. And even if you're in the US, pricing is incredibly vague. So if you're in the US, drop them an email for a quote for your chosen set. And for those of us outside – we'll keep you posted as soon as they become available here.


Great for colored pencils. May need to finish the tip with sandpaper but I haven't found a better sharpener for waxy drawing materials. Sharpen graphite now and then to lubricate. If tip gets caught in rotary mechanism, you can shake it out pretty easily.
They are especially nice with the Koh-I-Noor Progresso woodless pencils, sharpening them to a nice long point.
One of the most popular sharpeners among students and teachers at Art Center College of Design.
It has a cool retro vibe. The blue is an awesome color.


text:
Q: fstrim cronjob not working on Linux Mint 17.2 My /etc/cron.daily/fstrim looks like this:
#!/bin/sh
# call fstrim-all to trim all mounted file systems which support it
set -e

# This only runs on Intel and Samsung SSDs by default, as some SSDs with faulty
# firmware may encounter data loss problems when running fstrim under high I/O
# load (e. g.  https://launchpad.net/bugs/1259829). You can append the
# --no-model-check option here to disable the vendor check and run fstrim on
# all SSD drives.
exec fstrim-all --no-model-check

And yet trimming does not occur daily. I tested this by trimming, then writing ~30GB and deleting it, waiting for 24+ hours (~48 hrs in fact and barely used the computer) and trimming again with fstrim -v /. 45GB was trimmed on the 64GB disk.
What is wrong? Manual trimming seems to work, only the cronjob doesn't.
The drive is Samsung 830.

A: fstrim might trim all free space regardless whether it was already trimmed. This is the decision of the filesystem. So unless you are very sure about your filesystem's characteristics, as long as the value reported by fstrim does not exceed free space, is not an indication of an error.
As for testing whether something was trimmed, you could try: https://unix.stackexchange.com/a/85880/30851


text:
How Big Is A Fart? Science Has The Answer
Trending News: Here's How Much Room A Fart Occupies In Your Body
Because, like beautiful snowflakes, all farts are unique.
As part of the series "Science Question From A Toddler," science journalist Maggie Koerth-Baker has revealed that a fart in the human body can range in size from around the equivalent of a bottle of nail polish to a can of soda.
You can always trust kids to ask the best questions about the things that really matter in the world, such as: "How much space does a fart take in your body?"
As part of the "Science Question From A Toddler" series for FiveThirtyEight, science journalist Maggie Koerth-Baker addressed this epic question head on and shed some light into the world of fart studies (which, incredibly, is a real thing).
The article reveals that every fart is special, although (strangely) there hasn't been enough study to say whether every fart is unique, so it is hard to say how much space an average fart takes up in the human body.
Amazingly though there have been experiments on this crucial topic.
In 1997 a study of 16 Americans found a volume-per-fart range from 17 millilitres to 375 millilitres, which is the rough equivalent of the difference between a bottle of nail polish and a can of soda. We can only hope the latter fart was in a controlled, smell-proof environment.
The respected expert on fart science is Dr. Michael Levitt, a researcher at Minneapolis Veterans Affairs, and he has been conducting his valuable work since 1968.
Interestingly he believes that while we associate gas with being bloated, that isn't actually the case. Levitt is quoted as stating that only a small minority of people with bowel disorders can blame gas for bloating and that farting doesn't actually relieve bloating.
It is in fact the position of the diaphragm that causes the feeling of bloating. When your diaphragm, which is the muscle underneath the lungs, drops down lower, it pushes the stomach out and causes the uncomfortable bloated sensation.
Fernando Azpiroz, head of the department of digestive diseases at the University Hospital Vall d'Hebron in Spain believes that bloating is a learned behavior as a result of hypersensitive abdominal nerves, and so a patient could be trained to prevent it by holding their diaphragms up, in theory.
Finally, one more piece of crucial knowledge from a paper titled: "Beans, beans, the musical fruit, the more you eat the more you toot" backs up the commonly held belief that beans make us fart more. It was found that eating beans for breakfast more than doubled the volume of gas farted over the next six hours from an average of 260 millilitres to 660+.
And so that's pretty much everything you've ever wanted to know about farts, and probably a lot more. Good luck not thinking about farts the next time you pick up a can of Coke….
Should humanity put more time and funding into fart science?
Disrupt Your Feed
You'll never see (or smell) farts the same way when you start thinking about how big they are in the body.
The earliest recorded fart joke dates back to 1900 BCE and was an ancient Sumerian remark about a woman farting in her husband's lap.
Trending News: Booze May Help You Live Longer Than Exercise
Trending News: Science Says This Diet Is Twice As Effective For Weight Loss
Trending News: Listen To This Before Bed To Improve Your Sleep (And Memory)
Trending News: You'll Be Disgusted By How Much Pee There Is In Your Local Pool
Trending News: Scientists Are Working On A Urinal That Won't Splash Your Junk