1. I am being charged with a crime and I cannot afford an attorney. What can I do? If you are being charged with a crime, and you cannot afford to hire an attorney, you can apply for a public defender. If you are found eligible, you will be appointed an attorney to represent you. Please click here to submit your Indigency Screening Application online.
2. How do I find an attorney to represent me? Go to www.wsba.org or contact your local bar association.
5. Where do I go for a protection order? See http://www.ci.seatac.wa.us/index.aspx?page=549.
6. I just received a summons for jury duty, but I am unable or ineligible to serve, or I would like to delay my jury service. What can I do? Contact the court that sent the summons, and they will advise you. For SeaTac Municipal Court, call 206-973-4610.
7. Where can I obtain a copy of my driving record? Contact the Department of Licensing, http://www.dol.wa.gov/driverslicense/requestyourrecord.html .
8. How do I make payments on court imposed fines, costs, and assessments? For cases in SeaTac Municipal Court, you can make a payment on-line at nCourt. You may also submit payment by calling 1-800-701-8560, Monday through Friday 8:30 am – 9:00 pm, and on weekends from 9:00 am – 6:00 pm. Finally, you can contact the Court from 8:30 am to 4:00 pm Monday through Thursday and Friday 8:30 am - 12:00 pm by telephone (206-973-4610) or in person.
9. Where do I go to post bail or to post a bond? Contact the Court or the jail where the incarcerated person is located. For inmates held on a SeaTac charge, you can visit or contact the SCORE jail at 206-257-6200, http://www.scorejail.org , or contact SeaTac Municipal Court at 206-973-4610.
10. How do I find out if someone is in jail? Visit http://www.scorejail.org/inmate-locator/ . This link also contains a link to other county jail search engines.
11. I received a traffic (or parking) ticket. What can I do? Visit http://www.seatacwa.gov/government/municipal-court/traffic-and-parking-infractions and click on the appropriate link.
12. Where can I report a crime? In emergency situations, call 911 for assistance. For non-emergency situations, contact your local City Police Department or County Sheriff's Office. In SeaTac, the non-emergency telephone number is 206-296-3311. You may also file non-emergency reports on-line by following the appropriate links at http://www.ci.seatac.wa.us/index.aspx?page=174 .
13. How do I schedule an interpreter? If you have a case in SeaTac Municipal Court, let the court know that you require an interpreter, and advise the clerks what language you speak. The Court will schedule your hearing and provide an interpreter to assist you.
14. Where do I get a passport? Visit the passport website at travel.state.gov or call 1.877.487.2778 for information related to passports.
15. I would like to get married. Can a judge conduct the ceremony? Yes. An elected or appointed judge may conduct a marriage ceremony. Contact either the County Court or your local municipal court for a list of available judges, fees and requirements.
16. Where can I get my criminal history? Please contact the Washington State Patrol.
18. Where can I file a Small Claim action? Please contact King County District Court for information on filing a Small Claim action http://www.kingcounty.gov/courts/DistrictCourt/SmallClaims.aspx .
20. My court fines have been assigned to collection. Can they be recalled? To request that your fines be recalled from collection, please send your request in writing to the court. You may contact the court by clicking here.


NASA to Set a Space Base Close to the Moon?
NASA is said to be looking into creating an outpost in space close to the far side of the moon. NASA officials explained that the base could serve as a jumping point for exploration of the near-lunar region, asteroids, the moon and eventually Mars itself.
According to SPACE.com, NASA's associate administrator for human exploration and operations William Gerstenmaier wrote a memo saying that the space agency is setting up a team to develop a plan for exploring a spot in space known as Lagrangian points. These points in space are where the combined gravitational pull of two large masses (the Earth and the moon/sun) balance each other out rendering them a useful place to "park" a spaceship without using fuel for extended periods of time.
The specific Lagrangian point talked about in the memo is known as EML-2 and is located close to the far side of the moon. This spot might be a useful place to begin deep space exploration including voyages to asteroids, the moon itself, and eventually Mars and its moons.
NASA is looking to use partnerships already established during the construction of the international space station to help build the next space outpost. This time NASA will also be counting on the private sector early on to reduce costs and increase innovation. NASA will also be looking into reusable infrastructure and ways to make its projects more affordable over long periods of time in order to meet future budget restrictions.
The "EML-2" outpost – if created, will be the furthest location any human has ever visited (it will actually be 15% farther from Earth than the Apollo astronauts landed). Many technological hurdles will need to be overcome before such a human outpost can be created including but not limited to, finding ways to restrict human exposure to radiation over long periods of time so far away from Earth's magnetic field.
TFOT has reported many other stories regarding NASA's future plans including the Constellation Program – the Ares I rocket – back in 2008. We also covered many advanced spacecraft propulsion technologies, including an antimatter engine that uses positrons for fuel, a new privately developed lunar lander, an experimental supersonic combustion ramjet or scramjet, and tests of a methane-powered rocket engine.


text:
Updated on : Tuesday, January 12, 2021, 10:05 PM IST
Navi Mumbai: Traders' body GROMA welcomes SC order on Farm Laws
By Amit Srivastava
Picture for representation
Traders at the Agriculture Produce Market Committee (APMC) welcomed the Supreme Court order of suspension the implementation of the three farm laws until further notice and set up a committee comprising experts to hear the parties and understand the ground situation. They claimed that this is a half victory, and now the state government should also waive off the cess, being collected at the wholesale market for any trade.
Bhimji Bhanushali, secretary of the Grain, Rice & Oilseeds, Merchants' Association (GROMA) said that the Supreme Court order has come as a big relief for farmers as well as traders. "The farm laws are meant to end of the APMCs across the country," said Bhanushali. He added that the laws are against the livelihood of lakhs of traders and farmers.
Traders are opposing the farm laws at they feel that it is not providing them a level playing field. They claim that cess is being collected when a product is sold at the wholesale market while there is no such tax outside the market.
Navi Mumbai: NMMC steps into action after four birds found dead amid flu scare
"Big corporate will buy products outside the market at any price and over the period, the wholesale market will be wiped out," said Bhanushali. He added that in the future, farmers will have no option but to sell their produce to corporates at the price decided by the corporates.
In the first week of June, the central government amendment the Essential Commodities Act, 1955, brought The Farming Produce Trade and Commerce (Promotion and Facilitation) Ordinance, 2020'. With the amendment, farmers are allowed to trade outside the APMC without any State's tax or legal binding. Later, the government passed the bill in both houses.
At present, the trade of essential commodities like food grain, oil, vegetables, onion-potato attracts 0.8 % cess at the APMC. Bhanushali said that because of the cess, the same product becomes costly at APMC and people buy from out of the wholesale market. "We are not against the decision as we have always supported the welfare of farmers," said Bhanushali.
However, Bhanushali says that the decision has severely impacted their business and traders are incurring huge losses.
Navi Mumbai: Amidst ongoing farmers' protest, NCP's Rohit Pawar visits Vashi APMC at 4 am

redpajama_set_name: RedPajamaCommonCrawl


text:
Regione Autonoma de sa Sardigna
Capital Cagliari
President Renato Soru
(The Union)
Provinces Cagliari
Ogliastra1
Medio Campidano1
Olbia-Tempio1
Carbonia-Iglesias1
Municipalities 377
Area 24,090 km²
- Ranked 3rd (8.0 %)
Population (2003 est.)
- Ranked
11th (2.9 %)
68/km²
Map highlighting the location of Sardegna in Italy
Sardinia (Sardigna, Sardinna or Sardinnia in the Sardinian language, Sardegna in Italian, Sardenya in Catalan), is the second largest island in the Mediterranean Sea (Sicily is the largest), between Italy, Spain and Tunisia, south of Corsica. It forms part of Italy.
At the beginning of the nuragic age circa 1500 BC it was first called "Hyknusa" (latinized Ichnusa ) by the Greeks probably meaning island (nusa) of the Hyksos, the people who had just been expelled by Amasis I of Egypt circa 1540 BC and were by now looking for a new home . "Sandalyon" was her second name probably due to its shape, recalling a footprint. Last and present name has been Sardinia, for the Shardana , (who also had been expelled in Egypt by Ramses III circa 1180 BC )
3 Climate
4 Language
5 Business and commerce
8.1 Kingdom of Sardinia
Sardinia has an area of 24,090 km2 and a population of 1.65 million. Sardinia is an autonomous region of Italy. The regional capital is Cagliari. The region is divided into four provinces: Cagliari, Sassari, Nuoro and Oristano; another four provinces (Olbia-Tempio, Ogliastra, Carbonia-Iglesias and Medio Campidano) have been proposed to enter effect in 2005.
See also: Sardinian towns
Sardinia is one of two Italian regions whose inhabitants have been recognised as a "popolo" (i.e. a distinct people) by the Italian Parliament. The other region is Veneto.
The island contains numerous extraordinary tourist areas, including the Costa Smeralda and Gennargentu. The island is particularly famous for its beaches, but is also rich in other interesting places. why not also see: Tourist destinations of Sardinia
The climate is mainly Mediterranean, with a warm spring and fall, hot summer, and mild winter. Sardinia is suffering from a multi-year drought, thought by some to be due to global warming.
The most spoken languages in Sardinia are Italian and Sardinian, a Romance language with obscure roots in Phoenician, Etruscan language, and Near Eastern languages. While it has been significantly supplanted by Italian for official purposes, Sardinian is still widely spoken in rural areas.
In the northern regions of Gallura and Sassari, the language spoken is not Sardinian but a variety of Corsican (as in Corsica). In the island of San Pietro, the dialect spoken is Ligurian, from Genoa. In the city of Alghero in the north, a mediaeval dialect of Catalan is still spoken (the name of the city in Catalan is L'Alguer) as the island was a Catalan colony in the past.
Sardinia's currency (as a part of Italy) is now the Euro, but in some rural areas Sardinians still unofficially refer to su Francu (or loc. "su Pidzu"); 1 francu = 1,000 former Italian lire. "Unu Francu", referring to the now long-gone French franc, is a term used by older natives to mean some small amount, much as in English "It's not worth a farthing".
Several gold and silver mines operate on the island.
The Sardinian economy is today focused on tourism(peaking with the Costa Smeralda), industry, commerce, services and information technology; an increasing income is coming from its famous wines and gastronomy.
Trains on Sardinia connect the whole island but are rather slow. Some run on narrow gauge track. Many tourists catch the "trenino verde" which runs through the wildest parts of the island. It is slow but it allows the traveller to have scenic views impossible to see from the main road. The train connects Cagliari to Arbatax in the south and Sassari to Palau in the north. It's highly recommended to make the trip from Macomer to Bosa Marina, where the train winds its way through the typical Sardinian landscape to reach the sea near the coastal town of Bosa situated in the west of the island.
Sardinia is a precious natural resource, containing thousands of rare or uncommon animals and plant species such as the Mediterranean Monk Seal and the boar. It lacks many species instead, like the viper and the marmot, which are found everywhere else on the continent.
See also: History of Sardinia
Sardinia's history is very ancient. In 1979 human remains were found that were dated to 150,000 BC.
In Prehistory Sardinia's inhabitants developed a trade in obsidian, a stone used for the production of the first rough tools, and this activity brought Sardinians into contact with most of the Mediterranean people. Desiccated grapes, recently found in several locations, were DNA tested and proved to be the oldest grapes in the world, dating back to the Pyramids' and Mesopotamian's era. The Cannonau wine is made with these grapes and may qualify as the mother of all the European wines.
From Neolithic times till the Roman Empire, the Nuragic civilisation took shape on the island. Still today, more than 7,000 Nuraghe survive. It is speculated that, among others, the Shardana people landed in Sardinia coming from the eastern Mediterranean. Shardana had joined the Shekelesh and others to form the coalition of the Sea Peoples, but were defeated by Ramses III around 1180 BC in Egypt. Shardana and Shekelesh were also called by the Egyptians as the 'people from the faraway islands', implying that Shardana were already residents of Sardinia at the time of the Egyptian expedition. This assertion holds some truth, in fact most of the tombe dei giganti have a tombstone shaped like a ship vertically dug into the ground witnessing to their sea traveling activities. According to some linguistic studies, the town of Sardis in (Lydia) would have been their starting point from which they would have reached the Tyrrhenian Sea, dividing into what were to become the Sardinians and the Etruscans.
However most theories regarding the original population of Sardinia have been formulated prior to genetics research and in the traditional frame of east-west movements. Genetics has now shown that Sardinians are a pre-Indo-European population and, like Basque, different from all surrounding and much younger groups.
The density, extensiveness and sheer size of the architectural remains from the Neolithic period, points to a considerable population of the island.
Beginning around 1000 BC, Phoenician mariners established several ports of trade on the Sardinian coast. In 509 BC, war broke out between the native Nuragic people and the Phoenician settlers. The settlers called for help from Carthage, and the island became a province in the Carthaginian Empire. In 238 BC, after being defeated by the Roman Republic during the First Punic War, Carthage ceded Sardinia to Rome.
From 456 - 534, Sardinia was a part of the short-lived kingdom of the Vandals in North Africa, until reconquered by the Byzantine emperor Justinian I. Under the Byzantines, the imperial representative was a judge who governed from the southern city of Caralis. Byzantine rule was practically nonexistent in the mountainous Barbagia region in the eastern part of the island, and an independent kingdom persisted there from the sixth through ninth centuries.
Beginning in the eighth century, Arabs and Berbers began raiding Sardinia. Especially after the conquering of Sicily in 832, the Byzantines were unable to effectively defend their most distant province, and the provincial judge assumed independent authority. To provide for local defense, he divided the island into four Giudicati, Gallura, Logudoro, Arborea, and Caralis. By 900, these districts had become four independent constitutional monarchies. At various times, these fell under the sway of Genoa and Pisa. In 1323, the Kingdom of Aragon began a campaign to conquer Sardinia; the giudicato of Arborea successfully resisted this and for a time came to control nearly the entire island, but its last ruler Eleanor of Arborea, was eventually defeated by the Aragonese in the decisive Battle of Sanluri, June 30, 1409. The native population of the city of Alghero (S'Alighera in Sardinian, L'Alguer in Catalan) was expelled and the city repopulated by the Catalan invaders, whose descendants speak Catalan to this day. When Catalunia became part of Spain so did Sardinia.
Under Spain, Sardinians were regularly employed on the royal Spanish fleet. On October 7, 1571, at the Battle of Lepanto, Sardinian mariners on Board the admiralship of Infante Don John of Austria, brother of Felipe II, boarded the Turkish admiralship, overpowered the crew, and cut off the head of a Turkish admiral. The sight of the admiral's head on a spear put such a fear in the heart of the Turks, that they abandoned the fight and completely surrendered to Christians. This was the first time Turks lost out to Europeans signaling a trend of military decline and defeats from which Turks never recovered.
Kingdom of Sardinia
In 1720 Sardinia became an independent kingdom under the House of Savoy, rulers of Piedmont.
In 1792, Jean-Paul Marat, son of a Sardinian father and a Swiss mother, was one of the triumvirate leading the French Revolution.
In 1860, Vittorio Emanuele II, King of Sardinia became also the first King of Italy after conquering the rest of the peninsula.
The last four are new provinces since May 2005.
Sardinian language: Sardo logudorese, Sardo campidanese, Gallurese, Sassarese
History of Sardinia
Tourist destinations of Sardinia
Sardinian traditions
Famous people from Sardinia
Sardinian archaeological and artistic sites
An island kingdom near Sardinia
PALANTH www.palanth.com the International Journal of Palaeoanthropology is publishing the recent discovery of rock art on the Island of Sardinia from at least 13,000 B.C.. Stone Age people were traveling across the sea to this island thousands of years prior to Noah. Contact Nicholas Rolland, formerly University of Victoria, British Columbia Canada for more info
Official regional website in Italian
Official regional website in English
Map of Sardinia
Sardegna.com
ActivSardegna.Com (interactive maps of Sardinia)
Webzine about Sardinia
ItalianVisits.com
Archaeology and short history of Sardinia
Regions of Italy
Abruzzo | Basilicata | Calabria | Campania | Emilia-Romagna | Latium | Liguria | Lombardy | Marche | Molise | Piedmont | Apulia | Tuscany | Umbria | Veneto
Friuli-Venezia Giulia | Sardinia | Sicily | Trentino-South Tyrol | Aosta Valley
Sardinia is also a place in the State of Ohio in the United States of America; see Sardinia, Ohio.
Retrieved from "http://en.wikipedia.org/wiki/Sardinia"
Categories: Regions of Italy | Islands of Italy | Sardinia | NUTS 2 Statistical Regions of Europe


text:
Q: Starting Terminal as full screen by default In Mac OS X 10.8, how can I make terminal.app open in full screen by default?
It appears that it opens full screen if I control click the terminal app before quitting and then choose close. But I typically command + q to exit out of the application. How can I command + q the application, and the next time it opens have it be in full screen?

A: Another user posted this answer, but it appears they deleted it. You'll want to uncheck the "Close windows when quitting an application" option:

A: If you have disabled Resume, you could set Settings > Shell > Startup > Run command to osascript -e 'tell app "System Events" to keystroke "f" using {control down, command down}'.
In iTerm you can make new windows open in full screen by selecting Preferences > Profiles > Window > Settings for New Windows > Style > Fullscreen. If you haven't disabled Resume, full screen windows are restored in full screen like in Terminal.
iTerm also has an option to use custom full screen windows that don't have animations for changing spaces, don't disable ⌘H, and allow placing other windows over them. There is a bug where the menu bar is always shown in restored custom full screen windows though. If you haven't disable Resume from System Preferences, you could also run defaults write com.googlecode.iterm2 NSQuitAlwaysKeepsWindows -bool false.

redpajama_set_name: RedPajamaStackExchange


Watson WordPress Theme is a clean, trendy, modern and straight forward portfolio theme designed especially for professional and amateur photographers. It was designed and developed with the modern 21st century user in mind, that's why we took special care to make this theme compatible and easy to use on various devices and screen sizes – like touchscreen laptops, tablets smartphones and large size computer monitors.
Watson is for someone who is looking for a theme that can be set up easily and just works without any need to customize it.


Welcome to Future Kid Loyalty Club. You are more than just a customer to us, you are a privilege member and we shall treat you that way.
Welcome to Cartoon Network Loyalty Club. You are more than just a customer. To us, you are a privilege member and we shall treat you that way.
Welcome to Casper Scare School Loyalty Club. You are more than just a customer. To us, you are a privilege member and we shall treat you that way.


Bloomington complex bought by real estate firm in Portland, Ore.
Southpoint Office Center in Bloomington has been sold for $47.8 million, and the new owners said they would be investing in improvements to the property.
MSP Southpoint Equities LLC, based in Portland, Ore., bought the 15-story, three-building complex from FFII MN SP, a properties fund whose executive offices are in Minnetonka, according to a filing with the Minnesota Department of Revenue.
The fund that is selling the 366,807-square-foot complex on 82nd Street and Interstate 35W is associated with Founders Properties LLC, a privately held real estate investment company formed by Best Buy founder Richard Schulze and Opus founder Gerald Rauenhorst.
Since 1998, Founders has been involved in raising and managing 13 commercial real estate investment funds. Its portfolio of office, industrial, retail and mixed-use buildings spreads across 19 states and totals approximately 60 properties and more than 28.4 million square feet of space.
The buyer is part of Felton Properties Inc., a Portland-based real estate and ownership company with properties in five states, including Minnesota.
"From our standpoint, Southpoint offers tenants a prime location at the confluence of Highway 35 and 494. It is a landmark property in a location that is becoming more desirable with time," said Matt Felton, president of Felton Properties, in an e-mail.
Felton said the company would spend to improve its lobbies, entries, garage, fitness facility and cafe/lounge and will add more usable outdoor space.
"Felton Properties hopes to elevate the image of the property through a program of calculated improvements, along with an onsite office to enhance tenant service at the property," Felton added.
In 2016, Felton purchased the Berkshire Corporate Center, a Plymouth office center, for $23 million. Felton also owns the Braemar Office Park in Edina.
Earlier this year, Felton sold the 26-story Rand Tower in downtown Minneapolis to Chicago-based Maven Real Estate for $18.65 million.


Boiler Auxiliary Oversea Hot Sale Industrial Multi Fuel Steam Boiler Note: All boiler auxiliary can be purchased in separately. is a professional manufacturer of industrial boilers,biomass boiler ,autoclave, tire or plastic pyrolysis plant, waste oil distillation equipment.
Few boilers offer the flexibility of the Glenwood 7030 Residential Coal/Oil Boiler, built to commercial industrial standards and capable of burning wood, waste oils, and fuel oil. Glenwood 7030 Residential Wood/Coal/Oil Boiler. Glenwood Multi-Fuel Biomass Boiler - History & Features of The Glenwood Boiler.
Woodco's large multi-fuel boiler, by Danish Biomass Boiler manufacturing giant Justsen Energiteknik is available in 1MW – 5MW models. The ARGUS Flex by Justsen Energiteknik A/S range can burn virgin timber fuel and also Grades A and B waste wood , and is fully compliant with Renewable Heat Incentive (RHI) eligibility requirements.
Wood pellet boiler with multi fuel capability. Power output 35 kW DPBI range of multi fuel wood pellet boilers , are the biggest and heaviest in size per their power output when compared to other brands of pelletized fuel burners we currently stock.
multi fuel boilers. November 21, 2018, Pakistan-lng-boiler-for-sale, Leave a comment. Biomass steam boiler – Solid fuel boilers for sale … 2018-7-17 · Indeck LaMont solid fuel boilers are designed to fire biomass fuels for hot water generation.


text:
A Review of the Movie The Invisible Guest Directed by Oriol Paulo
The Invisible Guest (original title: Contratiempo) should figure among the best modern Spanish movies. We give it five stars and two thumbs up in all areas considered: story line, acting, directing. The Invisible Guest is a movie filled with characters who weave a tangled web of deception through lies, manipulation, power and influence. The most attentive viewer and listener is permitted to see and hear all that is unfolding. But can he interpret the two sides of the story? Can he unmask who the real monster is?
The performances of actors Mario Casas, Ana Wagener, José Coronado and Bárbara Lennie draw viewers into their lives, their lies, their deception, their misery, power and helplessness. But we see that the true power at work is that of the mind, the spirit and the will to discover the truth, that justice may be served. The promotional write-up of Contratiempo is most fitting. And this theatrical masterpiece should not disappoint even the most critical movie-goer. We read, "Accused of a murder of which he pleads innocent, the successful businessman Adrián Doria contracts the services of the country's finest witness trainer. In the course of a night, both will work to find a reasonable doubt that will save him from prison. The appearance of a new witness for the prosecution threatens to destroy the defense team's strategy, forcing them to work against the clock to rewrite the answers of a crossword puzzle that challenges all the laws of logic." We also read, "Every story has two sides...only one is true."
The same can be said about the meaning of life and the afterlife. Unlike the case with the movie The Invisible Guest, we might say there are several stories or theories to explain the meaning of life and what truly awaits us in the afterlife. However, all of those stories belong to only one of two sides: The one that is a lie, and the one that is truth. And one thing to keep in mind is that we are warned that the father of lies has the ability to appear as an angel. We read, "Satan himself transforms himself into an angel of light" (2 Corinthians 11:14 - The Holy Bible). Are you absolutely sure that your beliefs regarding God, life and the afterlife are based on truth?


Physical therapists utilize the pediatric LiteGait® partial-weight-bearing gait therapy device with a treadmill for children to improve their walking pattern and gait. The LiteGait® system can safely suspend a child to reduce the amount of weight-bearing on the lower extremities so the therapist can facilitate a more typical walking pattern. As time and therapy progresses, the child can bear more and more weight through their legs and feet, while the therapist assists less and less. Through repetition and practice the child's brain can "learn"a more functional walking pattern. Studies have shown that this form of therapy called, treadmill training, can help individuals establish a more functional gait. Studies also show that once a new pattern is established, it not only remains but can continue to improve with time.
Intensive Therapeutics already serves so many children with hemiplegia to improve upper extremity functioning. The gait training program can be combined with services to address lower extremity functioning to improve walking, running and interaction with their peers. All interested families are interviewed and screened to determine eligibility for the program. If you would like to discuss the program more, please fill out the form below and one of our trained staff will contact you.


Ferrari's 2014 campaign has been less than satisfying for the fans and the team alike, in order that they rectify the F14T's deficiences the team have set about conducting various tests during Free Practice sessions in the last few rounds.
One issue for Ferrari has been traction, which of course limits peformance not only due to the initial induced traction loss but also the ongoing speed of the car. Kimi Raikonnen was tasked with assessing a new geometry in Austin (seen above, captured by Giorgio Piola for Omnicorse). The team were also interested in capturing numerous aerodynamic measurements from the car, with equipment placed up and down the F14T.
As you can see great care has been taken by the team to minimise the test components aero impact on the car itself, as normally full fence pitot tube arrays would be used instead. Moreover it appears the aperatus is mainly focused on ride height and the centre of the car, with the aero map being taken care of from front to rear by a plethora of measurement devices.
Great info as always. Alonso wasn't involved in testing the 2015 components, I guess?
Indeed, only Kimi conducted the tests.


> 1) How do you affix barcodes to your slides, and where do you place them?
> the slide. Any suggestions?
> share? We're particularly interested if you use a keEMu database.
that Peter replied as well).
used on slides and how they create the tiny barcodes.
they can point you toward documents that share how NHM works this out.
place their barcodes (not how they decided though).
And, iDigBio did a Specimen Barcode and Labeling survey some time ago.
microscope slide images and slide data.


text:
\section{Introduction}

Neural network language models \cite{vaswani2017attention, radford2019language}
have been increasingly adopted as a central part of open-domain dialogue systems \cite{wolf2019transfertransfo, zhang2019dialogpt, roller2020recipes, adiwardana2020towards}. Utterances sampled from such language models sound natural, as reflected in these systems' high scores in human evaluations focused on measures such as ``engagingness'' or ``human-likeness''. While fluent, however, the responses generated by these systems are often only locally coherent or contain confabulated statements (see the red portions of the response in Figure~\ref{fig:hallucination} for illustration).

\begin{figure}[ht]
\centering
\includegraphics[width=\columnwidth]{figures/hallu_exp1.png}
\caption{An example of a hallucinated response generated by the GPT2 language model fine-tuned on the Wizard of Wikipedia dataset \cite{dinan2018wizard}. The response was conditioned on a passage of text from Wikipedia and the previous utterance. The response (in yellow) is on topic and appears to be plausible, but the information it contains is not supported by the document.\label{fig:hallucination}}
\end{figure}

In this work, we introduce a new classification task and benchmark for evaluating \emph{knowledge-grounded dialogue systems}, systems that are expected to conduct a conversation based on a particular source of information, with the goal of making unstructured information more accessible to a user. It is critical for such a system to avoid producing utterances that appear to convey information but in reality are not supported by the document or even contradict it. The system should also avoid responses that fail to respond to the user's question, because they are accurate but off-topic (e.g., \textit{``The University of Michigan is located in Ann Arbor"}), or because they are excessively general (e.g., \textit{``I don't know much about NYC"} in Figure~\ref{fig:hallucination}).

Existing automatic evaluation metrics for dialog, such as BLEU \cite{papineni2002bleu}, ROUGE \cite{lin2004rouge} and MAUDE \cite{sinha2020learning}, are ill-suited to detecting these issues, and  correlate poorly with human judgments \cite{liu-etal-2016-evaluate, dziri2019augmenting, sai2019re}. To facilitate progress towards reliable evaluation metrics for grounded dialog, we propose a new classification task extending the Natural Language Inference (NLI) paradigm \cite{dagan2005pascal, bowman-etal-2015-large, williams2017broad}. NLI seeks to determine, given a premise $p$ and a hypothesis $h$, whether $p$ entails $h$, contradicts it, or is neutral with respect to it.
For our taxonomy, we adopt the \emph{entailment} and \emph{contradiction} labels from the NLI paradigm, but split the \emph{neutral} label into three sub-categories:  \emph{hallucination} responses, which are topical but include unverifiable information; \emph{off-topic} responses; and \emph{generic} responses, which are too vague to be verified.
As a testbed for evaluation metrics based on this taxonomy, we create the Benchmark for Evaluation of Grounded INteraction (BEGIN), a dataset of 8113 dialogue responses generated by
language models fine-tuned on the Wizard of Wikipedia dataset \cite{dinan2018wizard}, and ask annotators to categorize these responses using the proposed taxonomy.

We establish baseline performance on this benchmark using two pretrained transformer models, BERT \cite{devlin2018bert} and T5 \cite{raffel2020exploring}. We fine-tune them on two existing NLI datasets: MNLI \cite{williams2017broad} and DNLI \cite{welleck2018dialogue}. As these NLI datasets only support the coarse-grained distinction between \emph{entailment}, \emph{contradiction} and \emph{neutral}, we additionally use perturbation techniques to generate examples for each of the five categories of our taxonomy, and fine-tune the pretrained models on this extended dataset. We find that there is considerable room for improvement between the best model we train (slightly above 70\%) and human performance estimated from inter-annotator agreement on the benchmark (around 90\% agreement with majority), suggesting that there are opportunities for developing stronger metrics for grounded dialog evaluation.

The main contributions of this work are:

\begin{enumerate}
    \item[(a)] We propose a taxonomy of responses generated by a knowledge-grounded conversation system; this taxonomy extends the Natural Language Inference framework.
    \item[(b)] We present a new benchmark, BEGIN, consisting of knowledge-grounded dialogue system responses annotated according to this taxonomy.
    \item[(c)] We establish baseline performance on BEGIN using BERT and T5 fine-tuned on standard NLI datasets, and improve upon it using our own adversarially-created dataset.
\end{enumerate}

\section{Constructing the BEGIN Dataset}
To generate dialogue responses for the BEGIN dataset, we fine-tuned two dialogue agents on an existing knowledge-grounded dialogue dataset. Based on error patterns commonly produced by these dialogue agents, we created a taxonomy of grounded dialog responses. We then constructed BEGIN by asking human annotators to categorize a sample of dialogue system responses according to this taxonomy.

\subsection{Training Knowledge-Grounded Agents}

\paragraph{Dataset} We fine-tune our knowledge-grounded dialogue models on the Wizard of Wikipedia dataset (WoW;  \citealt{dinan2018wizard}). WoW consists of crowdsourced English dialogues between a ``Wizard'' and an ``Apprentice'', where the goal of the Wizard is to convey to the apprentice information about a particular topic. The Apprentice, in turn, is expected to seek information about the topic. At each turn of the conversation, the Wizard is presented with passages from Wikipedia and chooses an evidence span (typically a sentence) to use as supporting evidence in their response. Not all utterances in WoW are grounded in external evidence: unlike the Wizard, the Apprentice is not presented with Wikipedia sentences when produce an utterance; and the Wizard is allowed to produce an utterance that does not use the evidence.

\paragraph{Task Formulation} We trained our models to generate the Wizard's response based on a concatenation of two inputs: an evidence span (the Wikipedia sentence presented to the Wizard) and the previous dialogue turn, produced by the Apprentice. We omitted (evidence span, previous turn, response) triples in which the Wizard did not explicitly select a passage as evidence for the response. We used 82722 triples for training, 8800 triples for development, and 8690 triples for test.

\paragraph{Models}
We fine-tuned the base GPT-2 model \cite{radford2019language} and the base version of T5 \citep{raffel2020exploring} on the WoW dataset. We filtered out responses that the Google Perspective API deemed to have a  greater than $50\%$ likelihood of containing toxic language.

For GPT-2, we framed response generation as language modeling (cf. DialogGPT, \citealt{zhang2019dialogpt}): we concatenated the evidence, the previous turn and the response, and continued training GPT-2 using cross-entropy over the response tokens. We used the same hyper-parameters as in \newcite{wolf2019transfertransfo}. We refer to this model as \textsc{GPT2-WoW}.

For T5, which is an encoder-decoder model, the encoder was provided with the concatenation of the evidence span and the previous turn, and the decoder was trained to predict each token in the response given the previous tokens. We used the hyperparameters found using grid search on the dev set. We refer to this model as \textsc{T5-WoW}. Neither of these fine-tuned models is intended to advance the state of the art in grounded dialogue generation; rather, we used them to investigate the errors made by typical neural dialogue systems.

\subsection{Response Taxonomy for Grounded dialogue Systems}
\label{sec:categories}
A manual inspection of 200 of the responses generated by \textsc{GPT2-WoW} revealed that, in addition to responses that were entailed by the document as desired, there were five common error types. This section defines and illustrates each of the six response types using examples from the manually inspected sample of responses.

\paragraph{Entailment} Entailed responses are those that are faithfully supported by the evidence span:
      \begin{quote}
        \footnotesize{\textsf{\textbf{Evidence}: \textit{Robert Norman Ross} (October 29, 1942 – July 4, 1995) was an  \textit{American painter}, art instructor, and \textit{television host}.}}
    \end{quote}
     \begin{quote}
        \footnotesize{\textsf{\textbf{Previous turn}: I would like to know more about \textit{Bob Ross}.}}
    \end{quote}
      \begin{quote}
        \footnotesize{\textsf{\textbf{Response}: Yes, absolutely! He was an \textit{American painter} and \textit{television host}}}.
    \end{quote}

\paragraph{Hallucination} Hallucinated responses are topical, but state information that cannot be verified using the source document, e.g.:

     \begin{quote}
        \footnotesize{\textsf{\textbf{Evidence}: \textit{The Tucson metro} area is served by many local television stations and is the $68^{th}$ largest designated market area (DMA) in the U.S. with 433,310 homes (0.39\% of the total U.S).}}
    \end{quote}
     \begin{quote}
        \footnotesize{\textsf{\textbf{Previous turn}: Have you ever heard about the \textit{Tuscon metro} area?}}
    \end{quote}
      \begin{quote}
        \footnotesize{\textsf{\textbf{Response}: Yes, one of the most visited \textit{US} cities is \textit{the Tucson metro} which was largely influenced by the \textit{television} culture}}.
    \end{quote}

We also include in the hallucination category responses that convey personal or subjective opinions, experiences, feelings, and so on, as such statements cannot be verified using the given evidence; some examples are \emph{I love to take walks in the park}, \emph{I've been to the hospital before}, or \emph{I think Justin Trudeau won the elections in 2015}. While such responses may be acceptable in human dialogues, in the context of a knowledge-grounded dialogue system we consider them to be hallucinations.

\paragraph{Generic} These are neutral sentences that are general enough to fit into a large number of possible contexts. Even if they are ostensibly about the same topic as the document, they are vague and do not provide information (true or not), e.g.:

    \begin{quote}
        \footnotesize{\textsf{\textbf{Evidence}: A \textit{ski} is a narrow strip of semi-rigid material worn underfoot to glide over snow.}}
    \end{quote}
     \begin{quote}
        \footnotesize{\textsf{\textbf{Previous turn}: \textit{Skiing} is really a lot of fun. Nothing like finding some fresh powder.}}
    \end{quote}
      \begin{quote}
        \footnotesize{\textsf{\textbf{Response}: It seems you might like \textit{skiing} in the winter.}}
    \end{quote}

\paragraph{Off-topic} In contrast to hallucinations, which are semantically close to the evidence but add small amounts of unverifiable information, off-topic responses are unrelated to the evidence, e.g.:

    \begin{quote}
        \footnotesize{\textsf{\textbf{Evidence}: \textit{Toyota} is the world's market leader in sales of hybrid electric vehicles, and one of the largest companies to encourage the mass - market adoption of hybrid vehicles across the globe.}}
    \end{quote}
     \begin{quote}
        \footnotesize{\textsf{\textbf{Previous turn}: Hi. I have never had a \textit{Toyota} but I know a lot of people like them.}}
    \end{quote}
      \begin{quote}
        \footnotesize{\textsf{\textbf{Response}: The era of \textit{manufactured fibers} began with the development of rayon in France in the 1890s.}}.
    \end{quote}

\paragraph{Contradiction} These are informative sentences that directly contradict the evidence, e.g.:

     \begin{quote}
        \footnotesize{\textsf{\textbf{Evidence}: Although most \textit{law schools} only offer the traditional \textit{three - year program}, several U.S. law schools offer an Accelerated JD program.}}
    \end{quote}
     \begin{quote}
        \footnotesize{\textsf{\textbf{Previous turn}: Because I did not want to go through all the schooling? Do you know how long \textit{law school} is?}}
    \end{quote}
      \begin{quote}
        \footnotesize{\textsf{\textbf{Response}: Most \textit{law schools} only offer a \textit{10 year program }}}.
    \end{quote}

\paragraph{Uncooperative responses}
Human dialogues are characterized by cooperative responses that follow the conversational expectations set out by Grice's maxims \cite{grice1989studies}: typical speakers do not intentionally mislead the listener, change the topic, or act unhelpfully in any other way. Dialogue  system responses may be inappropriate if they are not cooperative in this sense, even when they are entailed by the evidence document; for example:

    \begin{quote}
        \footnotesize{\textsf{\textbf{Evidence}: Starbucks was founded in Seattle, Washington in 1971.}}
    \end{quote}
    \begin{quote}
        \footnotesize{\textsf{\textbf{Previous turn}: I love eating at Starbucks, have you been there?}}
    \end{quote}
     \begin{quote}
        \footnotesize{\textsf{\textbf{Response}: The original location of Starbucks is in Seattle, which was founded in 1971.}}
    \end{quote}

Ultimately, dialogue evaluation metrics should take cooperativeness into account, and as such we asked our raters to score the cooperativeness of dialogue responses. For the time being, however, we limit the scope of our experiments below to evaluating faithfulness to the document, and leave modeling cooperativeness to future work.

\begin{table*}[ht]
\footnotesize
    \centering
    \begin{tabular}{l c l L{9.3cm}}
    \toprule
    Category & Frequency & Example \\
    \midrule
        Contradiction & 0.6\% &
        Evidence: & Broccoli is often boiled or steamed but may be eaten raw.\\
        && Previous turn: & It actually does. vegetable of course, like broccoli and spinach.\\
        && Response: & Broccoli can be eaten raw but not boiled
                            \\ \midrule
        Off-topic & 6.1\% &
        Evidence: &
        A programming language is a formal language that specifies a set of instructions that can be used to produce various kinds of output.
        \\
        &&
        Previous turn: & Is it programming?
        \\
        &&
        Response: & Yes and I think that is why the United States still has the highest population of any country
        \\ \midrule
        Generic & 27.5\% &
        Evidence: & Almost as soon as automobiles had been invented, races of various sorts were organised, with the first recorded as early as 1867.\\
        &&
        Previous turn: & That's so cool. I guess that's just our competitive spirit. \\
        &&
        Response: & That's what makes it such a interesting history to behold
        \\ \midrule
        Hallucination & 32.2\% &
        Evidence: & Generally, the heavier the ball, the more pins that will topple on two equivalent shots.\\
        && Previous Turn: & Yea but sadly bowling just isn't as demanding and difficult a sport as football so its fallen off while football is as prominent as ever.\\
        && Response: & When they first came out in the 70s, they used to have at least ten percent of the ball on two equivalent shots \\ \midrule
        Entailment & 33.6\% &
        Evidence: & Elvis Aaron Presley (January 8, 1935 – August 16, 1977) was an American singer, musician, and actor.\\
        &&
        Previous turn: & I like to listen to Elvis, the king of rock and roll. \\
        && Response: & 	Elvis Presley was born in 1935, and his death was August 16, 1977 \\
        \bottomrule
    \end{tabular}
    \caption{Examples of each of the categories from the development set of BEGIN, and their frequency in the development set. All responses were generated from GPT-2 fine-tuned on Wizard of Wikipedia based on the evidence and the previous turn shown in the table. Labels were annotated by human raters.}
    \label{tab:benchmark:distrib}
\end{table*}

\subsection{Annotation Procedure}

Rather than asking raters to explicitly classify responses based on the proposed taxonomy, we broke down the task into hierarchical questions with Likert scales (from~1 to~5). We summarize this procedure below, and provide the exact questions in Appendix~\ref{app:annotators}. Responses often consist of multiple sentences; because each sentence may display different degrees of faithfulness, we asked annotators to rate each sentence in the response separately.

First, we asked annotators to judge whether the response was about the same topic as the evidence, and if not, whether it was best described as generic or off-topic. We additionally asked them if the response was cooperative.
In the next stage, we asked the raters if the response was objective or contained personal and subjective opinions. If the rater judged that the response was objective, we asked them if in their judgment the response was intended to provide information, about the evidence in the document or anything else.
If the answer to the last question was affirmative, we presented them with two follow-up questions: first, we asked them if the response was fully supported by the evidence; and second, we asked if any part of the response contradicted the evidence.

We collected annotations for 8113 dialogue responses, which we split into a development (10\% of the examples) and test (90\% of examples) set; we release the data at \url{https://github.com/google/BEGIN-dataset}. Examples were randomly divided into dev. and test set partitions in such a way that examples using the same input context would only appear in the same partition. We did not create a training set to discourage the development of evaluation metrics that overfit to the specific features of BEGIN.

In post-processing, we converted the numerical ratings assigned by the annotators to one of our category labels using the procedure described in Appendix~\ref{app:cutoffs}. We note that the categories are not always mutually exclusive. For example, in a conversation about bees, the response \emph{They have pretty big personalities} would be both off-topic and generic. The appropriate label may also depend on linguistic ambiguity that cannot be resolved from the context given to the annotators.  In one example, the response \emph{Oppenheimer as he is known as I think in neonatal med / ophthalmology} was generated about a document that says \emph{He was the Director of Pediatric Neurosurgery at Johns Hopkins Hospital in Maryland from 1984 until his retirement in 2013}. Because the pronoun \emph{he} in the document doesn't resolve to an antecedent, it is hard to determine whether this utterance is better described as a hallucination (attributing a medical specialty not mentioned in the document) or off-topic (this document was probably not about Oppenheimer at all), which is what the rater ultimately selected.
Based on the annotations, 78\% of the generic responses and 71\% of the off-topic responses in our development set may also contain hallucinated information, but we label these overlapping cases as generic or off-topic respectively since these broader issues often subsume the hallucination problems.

We include examples from the development set in Table~\ref{tab:benchmark:distrib} along with the label breakdown.  We note that the labels are unevenly distributed. Hallucinations and generic comments make up two of the biggest categories of responses. By contrast, contradictions make up a small fraction of the distribution. This suggests that LM-based dialogue agents like \textsc{GPT2-WoW} and \textsc{T5-WoW} are more likely to add extra confabulated information rather than directly contradict the evidence.

To evaluate inter-annotator agreement, we obtained two additional annotations (from the same pool of raters) for approximately 15\% of the responses. The average Krippendorf's alpha on the responses to the different questions was around 0.41.  This denotes relatively low-to-moderate agreement. One factor that may impact the scoring is slight disagreements between Likert scores (such as the difference between a 4 and a 5) which were counted as ``partial'' agreements scaled by the distance between the responses. Further reducing agreement are cases where a disagreement in an earlier question propagated to the follow-up questions (e.g. they answered differently to whether the response was intended to be informative and so one of them left blank the follow-up question on how supported the information is) but we counted agreement on each of these questions as if they were independent of each other. Another factor that may affect human performance is ambiguous cases or instances where response categories are not always mutually exclusive, as mentioned above.

\section{Classifying Responses Using Existing NLI Datasets}
\label{sec:nli}
We establish baseline performance on BEGIN using models based on BERT \cite{devlin2018bert} and T5 \cite{raffel2020exploring}. We first fine-tune the models on existing NLI datasets. Since these datasets are only labelled for the traditional three-way NLI classification (entailment, contradiction and neutral), in these experiments we collapse the three sub-labels that correspond to \emph{neutral} in our fine-grained classification. In Section~\ref{sec:adv}, we train a classifier for the full 5-way categorization scheme by creating adversarial data.

\begin{table*}
\begin{subtable}{\linewidth}{
\footnotesize
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{ L{4.0cm} L{3.0cm} L{3.0cm} L{1.8cm} L{1.9cm}}
 \toprule
   \textbf{Evidence} &   \textbf{Previous Turn} &     \textbf{Response} &   \textbf{Method} &   \textbf{Label} \\
 \midrule
{The island's geography comprises relatively low-lying mountains surrounding a central plain, with several navigable rivers extending inland.}  &  {yes! is there many mountains there?} &  {The geography is mostly low lying mountains which surround a central plain and many rivers extending inland.} & None &  Entailment \\
 \midrule
  {A 2009 report found that the skateboarding market \textbf{is worth an estimated 4.8 billion} in annual revenue with 11.08 million active skateboarders in the world.}  &  I have heard that skateboarding is fun &  {The skateboarding market \color{red}{is not estimated to be 4.8 billion dollars.}} & {Response negation} & 	{Contradiction} \\
 \midrule
 {It is located on the Cumberland River in the \textbf{north central} part of the state.}&
 	I would really like to visit that river someday. &
 {It is also in the {\color{red}{south peripheral}} part of the state}
 &  {Antonym} &  {Contradiction} \\
 \midrule
   {\textbf{Elvis Aaron Presley} (January 8, 1935 – August 16, 1977) was an American singer, musician, and actor.}  &   {Elvis Presley was ahead of his time and really brought in a new era of music.} &  {I don't know much about music.} & {{GPT2-WoW} (with low temperature)} & {Generic} \\
  \midrule
   {The fia formula one world championship has been one of the premier forms of racing around the world since its inaugural season in 1950.	} &  {What is the formula 1 championship called?} & {\textcolor{red}{He is right.  i've been to one, but they have tvs set up.  the cars go fast though because there is minimum weight that includes driver.}} &  {Random utterance} & { Off-topic} \\

\bottomrule
\end{tabular}
}
}
\caption{Examples generated without perturbing the evidence (by perturbing the response, by generating a new response using GPT2-WoW, or by selecting existing responses from the dataset).}
\end{subtable}

\vspace{0.5cm}

\begin{subtable}{\linewidth}{
\resizebox{\textwidth}{!}{
\footnotesize
\centering
\begin{tabular}{ L{3.5cm} L{3.5cm} L{3.0cm} L{1.9cm} L{1.9cm}}
 \toprule
 \textbf{Original evidence} &    \textbf{Perturbed evidence} &   \textbf{GPT2-WoW response} &   \textbf{Method} &   \textbf{Label} \\
 \midrule
 {The \textbf{Chihuahua} is the smallest breed of dog and is named after the state of \textbf{Chihuahua} in \textbf{Mexico}.} &  {The Chihuahua is the smallest breed of dog and is named after the state of \textbf{Maine} in \textbf{Rochester}}. & {\textcolor{red}{The Chihuahua is known to be very friendly and Maine's mayor love them.}} & {Entity swapping} & {Hallucination } \\
\midrule
  {\textbf{Baking chocolate}, also called bitter chocolate, contains \textbf{cocoa solids} and cocoa butter in varying proportions, without any added sugars.} &  {\textbf{Cocoa solids}, also called bitter chocolate, contains \textbf{baking chocolate} and cocoa butter in varying proportions, without any added sugars.} & {\textcolor{red}{The consumption of coco solids has many benefits.}}& {Subject-object inversion} & {Hallucination } \\
 \bottomrule
\end{tabular}
}
}
\caption{\label{tab:evidenceperturbation}Examples generated by perturbing the evidence: the response is a hallucination with respect to the original evidence.}
\end{subtable}

\caption{Examples from the automatically-generated adversarial data. Bold text highlights segments in the source documents that are either supported, contradicted or neutral by the response. Red text highlights the justification for why the responses are annotated with a specific label.}
\label{tab:adversarial}
\end{table*}

The first dataset we use, MNLI \cite{williams2017broad}, is a collection of 433k premise-hypothesis pairs, where three hypotheses, one for each label, were generated by crowdworkers based on a premise drawn from a corpus. The second is DNLI \cite{welleck2018dialogue}, which consists of 343k  pairs of dialogue utterances and ``persona attributes'' curated from the PersonaChat dataset \cite{Zhang2018Personalizing} (e.g., a persona-describing attribute like \emph{I have two cats} may be contradicted by a conversation utterance saying \emph{I don't have any pets}). Each persona sentence and each utterance were associated with human-labeled triples (subject, predicate, object). A number of approaches were used to form NLI examples by linking dialogue utterances and persona sentences. For example, each unique pair of sentences that shared the same triple were labeled as entailment and each pair of sentences that were from contradictory triples were labeled as contradiction.

\section{Adversarially Augmented Training Set}
\label{sec:adv}

The experiments described in Section~\ref{sec:nli} were based on existing NLI datasets, which support a coarse-grained three-way classification, but not the full five-way taxonomy we introduced in Section~\ref{sec:categories}. In this section we introduce a strategy to automatically create ``silver'' training data for a classifier that produces the full taxonomy. We avoid training on BEGIN, because, as we mentioned before, we see it as a test-only dataset; recent work has shown that neural networks can overfit to irrelevant features of the dataset when trained on one part of it and tested on another part.
We generated a balanced dataset of 7900 (evidence, dialogue history, response) triples (i.e. each label constituted 20\% of the data), using the procedures described for each target label in the remainder of this section. See Table~\ref{tab:adversarial} for examples of our silver data.

\paragraph{Entailment} We use the original human generated responses, but to avoid opinions or subjective experiences, we subsample from the portion of examples where the response doesn't use first person pronouns (selected from a word list) and at least 25\% of the words in the response are in the evidence (to avoid responses that are only tangentially related to the evidence).

\paragraph{Off-topic} Off-topic responses are sampled from WoW responses that are based on other pieces of evidence. To avoid having off-topic responses that would be trivial to spot based on lexical cues, we sample from conversations that were about the same topic as the target conversation.

\paragraph{Generic} Generic sentences are generated from GPT2-WoW with a low softmax temperature (0.4).

\paragraph{Hallucination}
We perturb evidence spans from the WoW test set and then feed them to GPT2-WoW; in general, this results in responses that could be considered hallucinations with respect to the original evidence (see Table~\ref{tab:evidenceperturbation}).
We use three perturbation methods, each applied to a different evidence document. All of these perturbations substantially alter the truth of the sentence while keeping it on topic. First, we swap the subject and the object of the original evidence. Second, we replace up to two verbs in the sentences by verbs of the same tense. Finally, based on an error analysis that showed that most hallucination errors made by our dialogue system involved incorrect entities, we extract all mentioned entities from different dialogue examples using the SpaCy NER tagger \cite{honnibal2017spacy}, and replace up to two randomly chosen entities in the original evidence document with entities of the same type (e.g., Person, Location or Organization).

\paragraph{Contradiction}
Adversarially-generated contradiction examples include two types of cases. The first is negated sentences, created based on an English Resource Grammar (ERG) parse \cite{Fli:Ben:Oep:14}; for example, \emph{The skateboarding market is estimated to be around eight billion dollars} was replaced with \emph{The skateboarding market is \textbf{not} estimated to be around eight billion dollars}. In the second type of case, adjectives are replaced with their WordNet antonyms \cite{miller1998wordnet}: \emph{Ancient Greece was home to the \textbf{first} pentathlon} is replaced with \emph{Ancient Greece was home to the \textbf{last} pentathlon that was documented}.

Unlike hallucination examples, where we perturb the document and then feed it to GPT2-WoW, the contradiction examples are generated by directly perturbing the human response from the WoW dataset: initial experiments indicated that GPT2-WoW is not sensitive to these perturbations when applied to the evidence, in contrast with its sensitivity to the more substantial perturbation we used to generate hallucination examples. This indicates that this dialogue systems responses are only grounded in the document to a fairly limited extent.
\input{human_annotated_data}
\section{Experimental Set-up}
Each classifier takes as input the context---the evidence and the previous conversation turn---concatenated with a separating delimiter and the response.  For BERT, we train a three-way or five-way classifier over the output \texttt{[CLS]} token. For T5, we follow the MNLI set-up used in the paper that introduced T5 \cite{raffel2020exploring}: the string ``premise:~'' is concatenated with the context, the string ``hypothesis:~'' is concatenated with the response, and the concatenation of the two strings is then passed as input to T5.

In addition to separate experiments evaluating models fine-tuned on MNLI and models fine-tuned on our adversarially augmented training data, we also investigated the performance of models trained first on MNLI and then on our adversarial data.

All models were trained with a batch size of 32 over 3 epochs, using the Adam optimizer and a learning rate of $2\times10^{-5}$.
We evaluated the classifiers' performance via accuracy and macro-averaged F1 (i.e. computing F1 on each category before averaging) on BEGIN.

\section{Results}
Table~\ref{tab:results} summarizes the results of our experiments. MNLI is clearly a better fit to this task than DNLI. Fine-tuning on the adversarial data on its own is fairly effective even though it is a significantly smaller resource than DNLI or MNLI. Finally, fine-tuning first on MNLI and then on the adversarial data produces higher accuracy than training on the adversarial data alone.

None of our models exceeds 71\% accuracy in the three-way classification setting, or 46.5\% accuracy in five-way classification.  By comparing individual annotator ratings and the majority-voted label in the triple-annotated subset of BEGIN, we estimate that human accuracy is about 90\% for 3-way classification and 75\% for 5-way classification. While humans do not agree perfectly, there is still a lot of room for improvement between these models and human performance. Finally, we note that accuracy is similar across BERT and T5, despite the fact that T5 contains orders of magnitude more parameters and was pretrained on a much larger corpus. This suggests that scaling up the pretrained model may not be sufficient to make progress on this task using the training datasets we have explored.

\subsection{Error Analysis}
In Table~\ref{tab:exampleoutput}, we show some examples of errors made by different BERT-based models. One possible source of mistakes is misleading lexical cues.  In the first example, all three models label the response as entailed, which may be due to the high degree of lexical overlap with the evidence.  However, the semantic meaning of the information in the response is a bit different from the evidence, which is why humans annotated it as a hallucination.  In the second example, two of the BERT models misidentify the response as a contradiction, which may be due to the models relying too much on the negative word \emph{didn't} in the response.
\input{example_output}

\subsection{Label Confusion}
Figure~\ref{fig:confusion} compares the predictions of three fine-tuned versions of BERT to the gold labels. All three variants are poor at predicting contradiction. The model fine-tuned on MNLI over-predicted contradictions, while the model fine-tuned on the adversarial data has low recall for contradiction prediction. The model trained on both resources performed better at predicting contradictions, but still misclassified most of them as hallucinations. The off-topic examples are also difficult for the models to classify; while they are only rarely confused with contradiction or entailment, they were often confused with the other neutral subcategories (\emph{hallucination} and \emph{generic}).

All three models displayed a substantial degree of confusion between the \emph{hallucination} and \emph{entailment} labels, but not as much between \emph{entailment} and \emph{generic} or \emph{off-topic} examples. This difficulty of distinguishing hallucinated and faithful information would be obscured in the 3-way NLI categorization scheme where \emph{hallucination} is aggregated with \emph{off-topic} and \emph{generic}. In other words, our 5-way taxonomy makes it possible to identify this as a particular set of \emph{neutral} examples that is more likely to be confused with entailment.

\begin{figure*}[!htb]
\includegraphics[trim=2cm 7.2cm 1.6cm 5.5cm,clip,width=\textwidth]{figures/confusion.pdf}
\caption{Predictions vs. gold label on the development set for BERT fine-tuned on MNLI (left), our adversarially augmented data (middle), and MNLI followed by the adversarial data (right).}
\label{fig:confusion}
\end{figure*}
\section{Related Work}

\paragraph{NLI for dialogue system evaluation}
\newcite{welleck2018dialogue} propose using NLI to improve dialogue consistency. They create the dialogue NLI dataset, composed of (premise, hypothesis) pairs curated from the PersonaChat dataset \cite{Zhang2018Personalizing} and annotated with textual entailment labels by humans. They demonstrate the effectiveness of models trained on DNLI in re-ranking candidate responses by penalizing responses that contradict so-called ``persona sentences'', which express properties of the speaker (\textit{I am a vegetarian}).
  \newcite{dziri2019evaluating} also used NLI to evaluate dialogue consistency. They generated a large-scale, noisy synthetic dataset of (premise, hypothesis) pairs tailored for dialog, also based on \newcite{Zhang2018Personalizing}.

\paragraph{Hallucination in neural text generation}
The hallucination issue affects a range of tasks that involve generating text from a neural language model \cite{tian2019sticking,maynez2020faithfulness}. Across tasks, such models are typically trained to maximize the likelihood of the reference; at test time, this leads the decoder to produce an output with a high likelihood under the language model, regardless of whether the output is  faithful to the input \cite{holtzman2019curious, tian2019sticking}. Previous works attempting to quantify this issue have focused on the task of summarization. For example, \newcite{kryscinski2020evaluating} proposed a synthetic dataset for determining whether a summary is consistent with the source document. Similar to our adversarial training with noisily supervised examples, they train a classifier on a dataset constructed by applying a number of syntactic transformations to reference summaries. Besides the different target task (dialogue in our case), our work differs from \newcite{kryscinski2020evaluating} in two ways: first, we propose a fine-grained categorization of responses tailored for the dialogue task, inspired by a similar effort for abstractive summarization \cite{maynez2020faithfulness}; and second, we train an evaluation system using an adversarial dataset where responses result from perturbing the grounding document and feeding the result to a dialogue system.
An alternative approach for assessing faithfulness in abstractive summarization, which also uses an auxiliary language understanding task, measures whether a question answering system produces the same responses for the source and the summary \cite{durmus2020feqa,wang2020asking}.

\section{Conclusion}
In this paper, we introduced a new taxonomy for evaluating the faithfulness of knowledge-grounded systems. We presented the BEGIN benchmark for testing grounded dialog evaluation metrics, consisting of around 8k responses generated by two neural dialogue agents. Lastly, to establish baseline performance on this task, we fine-tuned BERT and T5 to classify a dialog response into one of the five categories of our taxonomy, using existing NLI datasets as well as adversarially created in-domain data. While this baseline performed reasonably well, there is significant room for future work to improve performance on our benchmark, which in turn will lead to stronger metrics for grounded dialog evaluation.

\subsection{Results}
  \begin{table*}[t]
\centering
\begin{tabular}{ l r r r r}
 \toprule
 \small \textbf{Method} & \small \textbf{Entailment} & \small \small \textbf{Neutral} & \small \textbf{Contradiction} & \small \textbf{Accuracy}\\
 \midrule
 {\small BERT-MNLI+ ctx} & { 0.62}  & 0.79  & \textbf{0.76} & 0.75 \\
 \small BERT-DNLI+ ctx & \textbf{0.89} & 0.71 & 0.03 & 0.60\\
\small BERT-Adversarial+ ctx& 0.82 & \textbf{0.94} & 0.45 & \textbf{0.79} \\
 \bottomrule

\end{tabular}
\caption{Accuracy (last column) and precision (rest of the columns) of models  tested on our manually-annotated test data. These models were trained on MNLI, DNLI and our coarse-grained adversarial training set. The input to the models corresponds to the concatenation of the fact, the history and the response.
 The performance differences between the models is statistically significant ($p< 0.01$) across all the classes.}
\label{tab:cxt-3way}
\end{table*}

 Table \ref{tab:mnli_vs_adver} shows the performance of the BERT model  fine-tuned on the adversarially generated data \texttt{(BERT-Adversarial)} versus the performance of the BERT model fine-tuned on MNLI \texttt{(BERT-MNLI)} and on DNLI  \texttt{(BERT-DNLI)}.

Significance is established via chi-squared tests with a threshold of $p<0.01$.
 We can observe that the adversarial data significantly improves the overall performance of the BERT classifier compared to the existing datasets. In particular, it improves detecting neutral cases ($+0.18$ increase vs. \texttt{BERT-MNLI} and $+0.28$ vs. \texttt{BERT-DNLI}). Also, it outperforms \texttt{BERT-MNLI} in predicting the entailment cases ($+0.21$ increase) and achieves competitive results w.r.t. \texttt{BERT-DNLI}.
 However, despite its success in recognizing entailed and neutral responses, \texttt{BERT-Adversarial} falls short in predicting contradictory responses, yielding lower precision than \texttt{BERT-MNLI}. This can be attributed to the quality of the automatically collected contradictory examples which do not align with real-case errors generated by GPT2-WoW.

 The relatively low performance of \texttt{BERT-MNLI} can be ascribed to two factors: First, MNLI focuses on classifying entailment between short and single sentence pairs, unlike our task where the responses can be composed of many utterances and the facts can contain many clauses. Second, there is a significant mismatch in sentence distributions between our adversarial data and MNLI in which examples are derived from image captions.
 On the other hand, although DNLI does not suffer from the domain-shift issue, it performs poorly in detecting neutral and contradictory responses, highlighting its inefficiency in evaluating grounded dialog models. While DNLI may resemble the domain of our adversarial grounded data, it is different in that it does not reflect grounded dialog data through its utterance-persona pairs. Also, it does not echo smooth grounded real conversations.

To summarize, we demonstrate that leveraging free and noisy data boosts the performance of the multinomial classifier. Further,
with roughly 2\% of the  MNLI and DNLI size (${\sim}8$K vs. ${\sim}430$K and ${\sim}340$K), the adversarial data shows its efficacy on grounded dialog data, outperforming existing NLI datasets without the need for manual collection of even a single label for training.

\section{Fine-grained Dialog Response Classification: Experiments}
\label{variation-adversarial}

We next fine-tune BERT on the fine-grained adversarially generated data. We experiment with two settings: one in which we limit the input to the facts and responses, omitting the history of the conversation from the classifier's input, as we did in Section~\ref{sec:threeway}; and one in which the history is included.  We aggregate \texttt{BERT-Adversarial}'s generic, off-topic and hallucination predictions into a single ``neutral'' label, to enable comparison with \texttt{BERT-MNLI} and \texttt{BERT-DNLI}.
\texttt{BERT-Adversarial} substantially outperformed \texttt{BERT-MNLI} and \texttt{BERT-DNLI} on the cases where the correct label was entailment (Table~\ref{multinomial-class-fact}). Accuracy on these cases was higher when \texttt{BERT-Adversarial} trained on the fine-grained five-way classification than when the three neutral categories---generic, off-topic and hallucination---were collapsed prior to training the classifier, as reported in Section~\ref{sec:threeway} (five-way: $0.89$; three-way: $0.79$).
This shows that teaching the classifier to differentiate between a larger number of classes led the model to learn representations of the response that are helpful for distinguishing entailment examples from all other types. Providing the context (i.e., history) further improved the accuracy on this category to $0.92$, and improved the overall accuracy of the trained classifiers. This suggests that some of the single-turn classification errors reflect false positives arising from short and underinformative facts. Additional context also improved the performance of classifiers trained on the coarse-grained data (Table~\ref{tab:cxt-3way}).

\texttt{BERT-Adversarial} also achieved very high accuracy in detecting off-topic responses, with or without context; this may be due to the fact that off-topic responses have high semantic distance from the context. Generic responses were also detected with high accuracy.
On the other hand, the classifier frequently assigned an incorrect label to the example for which the correct label was contradiction. We made two attempts to address this issue. First,
we expanded the adversarial training set with contradiction cases in which numerical entities in WoW test responses  were swapped with other numerical entities from the corpus and we trained \texttt{BERT-NumSwap} on this data. As Table~\ref{multinomial-class-fact} shows, this strategy increased the accuracy of predicting contradiction. However, the overall accuracy of the model declined, in particular when the correct label was entailment or hallucination. Second, we replaced the adversarially-generated contradiction examples with instances from MNLI where the correct label was contradiction, and fine-tuned BERT on this hybrid dataset (referred to in Table~\ref{multinomial-class-fact} as \texttt{BERT-Adver-ContraMNLI}). Again, MNLI instances were not a good match for our task: contradiction accuracy in this experiment dropped considerably from $0.46$ to $0.15$.
Contradiction in our case does not yet extend to implied contradictions. The gold test data actually contains a few such examples that are not provided through negation and adjective antonyms. The confusion matrix in Table \ref{confusion} shows that 38\% of the contradiction cases are being misclassified as entailment and 15\% as hallucination. While we could train with difficult contradiction examples that require reasoning, this would be costly, and we already have preliminary evidence that larger models pretrained on much more data can understand contradiction better.

\begin{table}[t]
\centering
\resizebox{\columnwidth}{!}{
\begin{tabular}{ l  r r r r r }
 \toprule
 \textbf{Prediction$\,\rightarrow$} &
\textbf{Ent.} & \textbf{Hal.} & \textbf{Gen.} & \textbf{Off.} & \textbf{Con.}  \\
\textbf{Gold$\,\downarrow$} \\
 \midrule
Entailment &   0.89 & 0.03 & 0 & 0 & 0.08\\
Hallucination & 0.04 & 0.57 & 0.04 & 0.33& 0 \\
Generic  & 0.1 & 0.2 & 0.7  & 0 & 0\\
Off-topic & 0 & 0.03 & 0  & 0.96& 0\\
Contradiction & 0.38 & 0.15 & 0 & 0 & 0.46\\
\bottomrule
\end{tabular}
}
\caption{Confusion matrix for BERT-Adversarial trained on the fine-grained adversarial data. }
\label{confusion}
\end{table}

\section{Discussion}
Progress in neural-network based document-grounded dialog systems requires reliable automatic evaluation metrics. Existing word-overlap and model-based metrics show poor correlation with human judgments \cite{liu2016not, sai2019re, dziri2018augmenting}. In this work, we proposed using the Natural Language Inference (NLI) paradigm to evaluate to what extent the system's responses are grounded in the document.

Existing NLI datasets are not suitable for this goal, for two reasons. Standard NLI labels are too coarse, and collapse several types of responses, including misleading ``hallucinated'' statements and relatively innocuous generic responses, into a single ``neutral'' label. Further, even when we focus on the distinction between entailed and non-entailed responses, existing datasets do not transfer well to the task and domain. To address this issue, we generated a weakly-supervised, within-domain adversarial dataset, by perturbing the grounding document and feeding it to a simple neural-network dialog system, based on the GPT2 language model. In an evaluation on a gold test set, we showed that a BERT-based classifier performs significantly better when trained on our dataset than when trained on existing NLI datasets (Tables~\ref{tab:mnli_vs_adver} and~\ref{multinomial-class-fact}). Training on the five-way classification we propose improved accuracy not only on the sub-labels of the ``neutral'' label, but also on the entailment label. This performance was attained despite the fact that our dataset was only 2\% the size of MNLI. We conclude that our expansion of the NLI framework, combined with the adversarial training method we proposed, is effective in evaluating the groundedness of a dialog system.

 \begin{table*}[t]
\centering
\begin{tabular}{ l c c c c}
 \toprule
 \small \textbf{Method} & \small \textbf{Entailment} & \small \textbf{Neutral} & \small \textbf{Contradiction} & \small \textbf{Accuracy}\\
 \midrule
\small BERT-MNLI & 0.59 & 0.74  & \textbf{0.80} & 0.72 \\
\small BERT-DNLI & \textbf{0.82} & 0.64  & 0.15 & 0.59 \\
\small BERT-Adversarial & 0.79 & \textbf{0.92} & 0.41 & \textbf{0.77} \\
 \bottomrule
\end{tabular}

redpajama_set_name: RedPajamaArXiv


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Philstocks, the online trading platform of Accord Capital Equities Corp. (formerly All Swerte Securities) began serving the Filipinos in terms of online stock brokerage in the year 2001. For as low as P5000, you can already open your account and enjoy the services Philstocks has to offer in your investment ventures. It also boasts its mobile trading application available for iOS and Android, suited for traders on-the-go, licensed and reviewed by securities regulatory institutions. Aside from that, they also have a Broker-Assisted Trading Service with available Certified Securities Representatives. All in all, Philstocks is indeed a great online trading platform both for beginners and experienced investors.
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text:
Vaccines | autism
> Senseih > Eugenics > Pathogens > Vaccines > Vaccines | effects
'Vaccines Cause Autism': District Attorney Nico LaHood. Bexar County District Attorney Nico LaHood said Monday he's ready for the backlash to his public comments that vaccines cause autism.
The father of a son with autism, LaHood voiced his opinions about the long-running issue for the documentary "Vaxxed: From Cover-up to Catastrophe" posted on the Autism Media Channel Facebook page. Sitting in his office, LaHood introduces himself as the D.A., then said, "I'm here to tell you vaccines can and do cause autism." The theory has been debunked in the medical world, but many parents looking for answers, remain skeptical. LaHood said he and his wife noticed a change in their son after he received his 18-month vaccination. "We had a very different child," he said. Asked whether he should be voicing his opinion due to his political office, LaHood said, "My opinions are just my opinions.
He said both President Barack Obama and former Gov. LaHood said, "I'm not forcing my opinion on you. 'Autism' Reported as Vaccine Side Effect, FDA Insert Shows. Discontinued vaccine contained thimerosal, aluminum and formaldehyde Adan Salazar Prison Planet.com March 31, 2016 A vaccine insert available via the US Food and Drug Administration website lists "autism" as one of many reported severe adverse side effects.
Sanofi Pasteur's whooping cough vaccine Tripedia, which was designed to be administered in five doses between six weeks and seven years of age, is no longer available on the market, but the serum's insert – last updated in December 2005 – provides hints to why it may have been pulled. Under a section on page 11 describing "ADVERSE REACTIONS," researchers acknowledged "autism," among other serious complications, had been reported following the vaccine's administration. A d v e r t i s e m e n t. U.S. Government Concedes That Mercury Causes Autism.
The U.S. government has concluded that childhood vaccines contributed to symptoms of autism in 9-year-old Hannah Poling.
The unprecedented concession was in response to one of three test cases that allege the mercury-containing vaccine preservative thimerosal caused autism in children. According to the case, the child was healthy and developing normally until her 18-month well-baby visit, where she received vaccinations for nine diseases, two of which contained thimerosal. Within 48 hours of the shots, Hannah become ill, refused to walk and could not sleep through the night. Within three months, she began showing signs of autism. According to U.S. Although federal officials continue to say there is no link between autism and vaccines, this concession says otherwise. The U.S. There are currently 4,900 autism cases pending in Federal "Vaccine Court. " Protect Your Right to Informed Consent and Defend Vaccine Exemptions THINK GLOBALLY, ACT LOCALLY.
Internet Resources Where You Can Learn More. Vaccines. The groundbreaking research findings presented at the International Meeting for Autism Research (IMFAR) in London, England, have revealed that young macaque monkeys given the typical CDC-recommended vaccination schedule from the 1990s, and in appropriate doses for the monkeys' sizes and ages, tended to develop autism symptoms.
Their unvaccinated counterparts, on the other hand, developed no such symptoms, which points to a strong connection between vaccines and autism spectrum disorders. This development which deconstructs mainstream myth that vaccines are safe and pose no risk of autism, was brought on by after studies on the type of proper safety research on typical childhood vaccination schedules that the U.S. Eightfold autism risk. MMR vaccination - eightfold autism risk A re-run of the 2002 Danish MMR vaccination - autism study [2], but this time also including children aged five and over, rather than cutting off at four years old, found that the Danish autism rate had in fact risen eightfold over the period since the introduction of the MMR vaccination. [3] The children aged five and over were added in because, in Denmark, a diagnosis of autism is never made until the age of five, so you would not expect to find any diagnoses of autism in the four and unders.
Even were greater awareness and better diagnosis of autism judged to account for half of these cases (a very generous allowance), that would still leave an extremely significant fourfold increased risk. Monkeys Get Autism-like Reactions to MMR & Other Vaccines In University of Pittsburgh Vaccine Study. A University of Pittsburgh study showed vaccines altered the behavior in monkeys.
Someone did perform safety studies the U.S. Centers for Disease Control and Prevention (CDC) and the U.S. Food and Drug Administration (FDA) should have mandated be performed and vetted BEFORE numerous vaccines were released into the public sector for mass vaccinations. Lead investigator Laura Hewitson, PhD, probably dropped a bombshell when she and her colleagues completed a macaque monkey (primates) study of the very same vaccines given to children during 1994-1999, i.e., the Measles-Mumps-Rubella (MMR) vaccine and several Thimerosal mercury-containing vaccines injected into children during that time frame when the autism spectrum disorder skyrocketed. Congress needs to consider seriously the Hewitson, et al. report that stated: That alone should be the explicit reason for duplicating the monkey study with independent non-pharmaceutical industry conflict of interest scientists. CDC forced to release documents showing they knew vaccine preservative causes autism.
For nearly ten years, Brian Hooker has been requesting documents that are kept under tight wraps by the Centers for Disease Control and Prevention (CDC).
His more than 100 Freedom of Information Act (FOIA) requests have resulted in copious evidence that the vaccine preservative Thimerosal, which is still used in the flu shot that is administered to pregnant women and infants, can cause autism and other neurodevelopmental disorders. Dr. Deadly Immunity. In June 2000, a group of top government scientists and health officials gathered for a meeting at the isolated Simpsonwood conference center in Norcross, Ga.
Convened by the Centers for Disease Control and Prevention, the meeting was held at this Methodist retreat center, nestled in wooded farmland next to the Chattahoochee River, to ensure complete secrecy.
Related: You Should Know This - Influenza - Vaccines - 1) Contro di noi: guerre, psyops, razzismo, sfruttamento & altro - vaccine safety - Vaccines - Studies Discussions - Vaccines - Mmr - Vaccines 3 - Autism - Vaccination - Education - Enterprise


text:
package org.lockss.laaws.mdq.client;

import java.nio.charset.Charset;
import java.util.Base64;
import org.springframework.http.MediaType;
import org.springframework.web.client.RestTemplate;
import org.lockss.util.rest.RestUtil;
import org.springframework.http.HttpHeaders;

/**
 * A base client for all of the LAAWS-MDQ web service operations.
 */
public class BaseClient {
  private static final String userName = "lockss-u";
  private static final String password = "lockss-p";

  private static final String REST_PORT = "24650";
  protected static final String baseUri = "http://localhost:" + REST_PORT;

  /**
   * Provides a RestTemplate that does not throw exceptions when the received
   * status code is not 2xx.
   *
   * @return a RestTemplate that does not throw exceptions when the received
   *         status code is not 2xx.
   */
  protected static RestTemplate getRestTemplate() {
    RestTemplate template = RestUtil.getRestTemplate();

    return template;
  }

  /**
   * Provides the basic HTTP headers to be used in a request.
   *
   * @return a HttpHeaders with the Basic Authorization and Content Type
   *         headers.
   */
  protected static HttpHeaders getHttpHeaders() {
    HttpHeaders headers = new HttpHeaders();
    headers.setContentType(MediaType.APPLICATION_JSON);

    String credentials = userName + ":" + password;
    String authHeaderValue = "Basic " + Base64.getEncoder().encodeToString(
	credentials.getBytes(Charset.forName("US-ASCII")));
    headers.set("Authorization", authHeaderValue);

    return headers;
  }
}

redpajama_set_name: RedPajamaGithub


text:
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GriBank Technology improves efficiency of electrical grids and maximizes the value, availability, usability and cleanliness of electricity.
TSG and Alevo will form joint ventures in strategic locations to open up new markets and advance sales of GridBanks to private and state owned utility companies.
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The eHealthCard – Medical records anytime and anywhere. Immediate access means faster diagnosis and better care.
TSG Global Holdings is delighted to announce its partnership with NCORD Healthcare LLC. NCORD is the innovator of the Electronic Health Record System called the "eHealthCard." eHealthCard is a cloud-based technology developed in compliance with the latest American and International HITECH, HIPAA and HL7 guidelines.
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Ethane burns with 80 percent fewer emissions than heavy oils and coal.
TSG Global Holdings has announced its strategic partnership with American Ethane and Amshale, for the marketing and sale of ethane, as well as, the engineering, procurement, construction (EPC) and ownership of infrastructure for the receipt, delivery and utilization of ethane in select markets around the world.
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TSG Global Holdings realizes that America is facing a critical challenge:
Welcoming immigrants from around the world who seek freedom and opportunity, while identifying and preventing entry of criminals, human traffickers, and persons deemed to pose a risk to national security.
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There are over 17,600 high quality apartment units, with shops, schools and clinics on 6.5 million square meters of land and a total project value of over USD 1.7 billion.
KHAZAR ISLANDS
Shopping and business centers
Amusements and recreation facilities
CAP CANA RESORT
Cap Cana Marina Resort consists of a 1740 room all-inclusive Hotel & Casino built on 500,000m2 with 700m beach-front, owned by the Mexican Moon Palace chain.
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TSG Global holdings, along with our strategic partners, has been active participants in Africa development for approximately twenty years.
We have worked with many US and foreign government agencies, multinational and regional corporations, NGOs and media organizations and enjoys an excellent reputation and relationship with the U.S. government, Congressional leaders and with many global leaders and investors, necessary to successfully execute complex projects.
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HURRICANE KATRINA RELIEF
Base Camp Management
Emergency Sewage Removal
MAGPLANE
Magnetic Levitation & Propulsion
Magplane is a ground-breaking magnetic levitation and propulsion transportation system, capable of transporting 30,000 passengers up to 500 km per hour, per direction. To relieve the over-population and traffic conjestion of its eastern seaboard, China has chosen the Magplane system as part of its plan in constructing a new transit infrastructure.
COMAG
Water Purification Technology
The urgent need for water purification and treatment facilities presents TSG with an opportunity to introduce a new and innovative advanced water purification technology, the CoMag Process. CoMag is an award-winning water treatment process that utilizes magnetically enhanced coagulation for the removal of contaminants from water in less time and space requirement, as well as with much lower capital, operation, and maintenance costs.
Fixed Site Security
Protective Security Detail
Convoy Security
SHOOTINGLAND
A Comprehensive Franchise & Entertainment Center
TSG Global Holdings has entered a partnership with former shooting champion, gamer, enthusiast and amateur in shooting games, Mr. JJ Rendón. Shootingland is a comprehensive franchise and entertainment center where people of all ages can truly live an extreme and unique experience.
TSG Global Holdings
1300 Pennsylvania Ave NW, Suite 700
Washington, DC 20004 U.S.A.
Telephone: 202-331-9320 Facsimile: 202-331-9321
info@tsg-globalholdings.com


The regular camp day is from 9:00 am-4:00 pm.
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Sensory impulse - Visual Dictionary - Copyright © 2005-2016 - All rights reserved.
[img]https://infovisual.info//storage/app/media/03/img_en/040 Sensory impulse.jpg[/img] [url=https://infovisual.info/en][/url] - [url=http://www.infovisual.info/]Visual Dictionary[/url] - Copyright © 2005-2016 - All rights reserved.
Sensory impulse: phenomenon of polarization and depolarization through which the abnormal acticity of a fibre spreads through a nerve.
Sensory neuron: nerve cord carrying sensory stimuli to the brain.
Dendrite: cytoplasmic extension of a nerve cell.
Cell body: part of the nerve cell between the axon and the dendrite.
Axon: extension of a neuron.
Motor neuron: nerve that produces muscular activity.
Motor end plate: muscular organ that becomes active in response to stimuli.
Myelin sheath: envelope of phosphorated fats.
Receptor (free nerve ending): receiver of nervous stimuli.