when did the ottoman empire surrender in ww1

Minority rights

The 1856 Congress of Paris paid special attention to the status of Jews and Christians in the Ottoman Empire. In Britain, William Gladstone made the massacres of Bulgarians by the Ottoman Empire a major campaign issue and demanded international attention. The Congress of Berlin in 1878 dealt with the status of Jews in Romania, especially, and also Serbia, and Bulgaria. On the whole, the 19th-century congresses failed to impose significant reforms.
The first minority rights were proclaimed and enacted by the revolutionary Parliament of Hungary in July 1849. Minority rights were codified in Austrian law in 1867.
Russia was especially active in protecting Orthodox Christians and Slavic peoples under the control of the Ottoman Empire.  However the Russian government tolerated vicious pogroms against Jews in its villages.  Russia was widely attacked for this policy.  By contrast there was little or no international outrage regarding the treatment of other minorities, such as black people in the southern United States before the 1950s when African colonies became independent.
Before the World War I, only three European countries declared ethnic minority rights, and enacted minority-protecting laws: the first was Hungary (1849 and 1868), the second was Austria (1867), and the third was Belgium (1898). In the pre-WW1 era, the legal systems of other European countries did not allow the use of European minority languages in primary schools, in cultural institutions, in offices of public administration and at the legal courts.
Minority rights at the Paris Peace Conference of 1919

Ottoman Empire in World War I

The Ottoman Empire was one of the Central Powers of World War I. It entered the war on 29 October 1914 with a small surprise attack on the Black Sea coast of Russia, which prompted Russia to declare war on 2 November 1914. Ottoman forces fought the Entente in the Balkans and the Middle Eastern theatre of World War I. The Ottoman Empire's defeat in the war in 1918 was crucial in the eventual dissolution of the empire in 1922.
Entry into World War I
The Ottoman entry into World War I was the result of two recently purchased ships of its navy, still manned by their German crews and commanded by their German admiral, carrying out the Black Sea Raid on 29 October 1914. There were a number of factors that conspired to influence the Ottoman government and encourage them to enter the war. The political reasons for the Ottoman sultan's entry into the war are disputed, and the Ottoman Empire was an agricultural state in an age of industrial warfare. Also, the economic resources of the empire were depleted by the cost of the Balkan Wars of 1912 and 1913. The reasons for the Ottoman action were not immediately clear.


Gaming Stackexchange

Q: Can you play minecraft on Wii U and Xbox together on mulitplayer? I want to play with my friend but don't know how!

A: What you are referring to is called "Cross Platform Multiplayer" Where two or more players on different consoles can play the same game. Currently, Cross platform multiplayer is very rare, and few games have this feature available.
There have been rumors that improved Cross platform multiplayer may be included at a future date on some or all of the minecraft versions, but this is not yet confirmed.(http://www.ign.com/articles/2015/08/05/gamescom-2015-mojang-wants-minecraft-to-work-cross-platform)
Unfortunatly, at the moment, You can only play Minecraft online multiplayer if you, and everyone you are trying to play with are using the same version of minecraft on the same console or platform.
*EDIT
As Pointed out by colorfusion in the comments, Minecraft Pocket Edition is compatible with Minecraft Windows 10 Edition. (Here's an article about it http://www.pcworld.com/article/2982932/software-games/minecraft-pocket-edition-now-lets-windows-10-ios-and-android-gamers-play-together.html)


A novel approach for knowledge-grounded dialogues by improving knowledge selection with a sequential latent variable model.

Title: Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue

Abstract: Knowledge-grounded dialogue is a task of generating an informative response based on both discourse context and external knowledge. As we focus on better modeling the knowledge selection in the multi-turn knowledge-grounded dialogue, we propose a sequential latent variable model as the first approach to this matter. The model named sequential knowledge transformer (SKT) can keep track of the prior and posterior distribution over knowledge; as a result, it can not only reduce the ambiguity caused from the diversity in knowledge selection of conversation but also better leverage the response information for proper choice of knowledge. Our experimental results show that the proposed model improves the knowledge selection accuracy and subsequently the performance of utterance generation. We achieve the new state-of-the-art performance on Wizard of Wikipedia (Dinan et al., 2019) as one of the most large-scale and challenging benchmarks. We further validate the effectiveness of our model over existing conversation methods in another knowledge-based dialogue Holl-E dataset (Moghe et al., 2018).


Paper exploring algorithmic fidelity in language models for social science research.

Title: Analysis and modeling of scale-invariance in plankton abundance

Abstract: The power spectrum, $S$, of horizontal transects of plankton abundance are often observed to have a power-law dependence on wavenumber, $k$, with exponent close to -2: $S(k)\propto k^{-2}$ over a wide range of scales. I present power spectral analyses of aircraft lidar measurements of phytoplankton abundance from scales of 1 to 100 km. A power spectrum $S(k)\propto k^{-2}$ is obtained. As a model for this observation, I consider a stochastic growth equation where the rate of change of plankton abundance is determined by turbulent mixing, modeled as a diffusion process in two dimensions, and exponential growth with a stochastically variable net growth rate representing a fluctuating environment. The model predicts a lognormal distribution of abundance and a power spectrum of horizontal transects $S(k)\propto k^{-1.8}$, close to the observed spectrum. The model equation predicts that the power spectrum of variations in abundance in time at a point in space is $S(f)\propto f^{-1.5}$ (where $f$ is the frequency). Time series analysis of local variations of phytoplankton and zooplankton yield a power-law power spectrum with exponents -1.3 and -1.2, respectively from time scales of one hour to one year. These values are roughly consistent with the model prediction of -1.5. The distribution of abundances is nearly lognormal as predicted. The model may be more generally applicable than for the spatial distribution of plankton. I relate the model predictions to observations of spatial patchiness in vegetation.

Title: Neural language representations predict outcomes of scientific research

Abstract: Many research fields codify their findings in standard formats, often by reporting correlations between quantities of interest. But the space of all testable correlates is far larger than scientific resources can currently address, so the ability to accurately predict correlations would be useful to plan research and allocate resources. Using a dataset of approximately 170,000 correlational findings extracted from leading social science journals, we show that a trained neural network can accurately predict the reported correlations using only the text descriptions of the correlates. Accurate predictive models such as these can guide scientists towards promising untested correlates, better quantify the information gained from new findings, and has implications for moving artificial intelligence systems from predicting structures to predicting relationships in the real world.


Mette Frederiksen

2019 election The 2019 general election saw the Social Democrats gaining a further seat while support for the Danish People's Party and the Liberal Alliance collapsed, costing Lars Løkke Rasmussen his majority. With the result beyond doubt on election night, Rasmussen conceded defeat. Frederiksen was appointed prime minister on 27 June 2019, heading an exclusively Social Democratic minority government supported by the red bloc of the Social Liberal Party, the Red-Green Alliance and the Green Left. Despite having run on an anti-immigration stance during the election, Frederiksen briefly shifted her stance on immigration by allowing more foreign labour and reversing government plans to hold foreign criminals offshore after winning government.
2020 mink cull
At a press conference on 4 November 2020, Mette Frederiksen stated that the government had decided that all mink in Denmark should be killed due to the risk of infection with COVID-19. Subsequently, it emerged that this order was illegal and was described by many as being in violation of the Danish Constitution. The government came up with changing explanations and several parties in the Folketing demanded an account of the mink case. The statement was published on 18 November 2020 and it emerged that six ministers had been warned on 1 October 2020 that the order was illegal. Minister of Food, Agriculture and Fisheries Mogens Jensen withdrew immediately. Mette Frederiksen has denied knowledge of the lack of legal basis.

Hans Christian Gram

Gram stain In Berlin, in 1884, Gram developed a method for distinguishing between two major classes of bacteria. This technique, known as Gram staining, continues to be a standard procedure of medical microbiology. This work gained Gram an international reputation. The staining method later played a major role in classifying bacteria. Gram was a modest man, and in his initial publication he remarked, "I have therefore published the method, although I am aware that as yet it is very defective and imperfect; but it is hoped that also in the hands of other investigators it will turn out to be useful."
A Gram stain is made using a primary stain of crystal violet and a counterstain of safranin. Bacteria that turn purple when stained are termed 'Gram-positive', while those that turn red when counterstained are termed 'Gram-negative'.
Other work
Gram's initial work concerned the study of human red blood cells. He was among the first to recognise that macrocytes were characteristic of pernicious anaemia.
During 1891, Gram taught pharmacology, and later that year was appointed professor at the University of Copenhagen. In 1900, he resigned his professorship of pharmacology to become professor of medicine. As a professor, he published four volumes of clinical lectures which became used widely in Denmark. He retired from the University of Copenhagen in 1923, and died in 1938.
Popular recognition
On 13 September 2019, Google commemorated the anniversary of his birth with a Doodle for Canada, Peru, Argentina, Australia, New Zealand, Israel, India and some European countries.
Personal life
Gram's great-granddaughter Lone Gram is a microbiologist who works at the Technical University of Denmark.


Hvilke organisationer har Mette Frederiksen forbindelse til?

Mette Frederiksen

Member of Folketing Frederiksen worked as a youth consultant for LO, The Danish Confederation of Trade Unions. She was elected as a member of parliament for Copenhagen County in the 2001 general election which saw the Social Democrats losing the first place and placing second for the first time since 1920. After her election, Frederiksen was named as her party's spokesperson for culture, media and gender equality. In 2002, she received the Nina Bang award for "showing political courage, enthusiasm andimpact with social feeling".  Frederiksen received the Ting Prize in 2012.  She co-authored the books Epostler (2003) and From Fight to Culture (2004).
After the 2005 general election loss, Frederiksen became her party's spokesperson for social affairs. Following the election, she also served as the vice-chairperson of the parliamentary group of the Social Democrats. In the 2007 general election that saw the Social Democrats losing two seats, Frederiksen obtained 27,077 votes, placing her seventh in the ranking of the ten Danish politicians with the most votes.
After the 2011 general election that led to a Social Democrats government, Frederiksen served under Prime Minister Helle Thorning-Schmidt as Minister for Employment from 2011 to 2014 and Minister of Justice from 2014 until she succeeded her as party leader. As Minister of Employment, Hendriksen sought reforms of early retirement pensions, flex jobs, and the employment system. The controversial cash assistance reform meant lower cash benefits for young unemployed and provided cohabiting mutual support, among other things.
Leader of the Social Democrats
Under Frederiksen's leadership after the 2015 general election in which the Social Democrats returned to power and gained three seats in the Folketing, the party has moved back to the left on economic issues while taking a conservative stance on immigration.
Prime Minister of Denmark


Venstre (Denmark)

(V; , ), full name  (), is a conservative-liberal, agrarian political party in Denmark. Founded as part of a peasants' movement against the landed aristocracy, today it espouses an economically liberal, pro-free-market ideology.


Which country won the most gold medals in the olympic history

List of Olympic teams by medals won

The United States has placed first overall in the Summer Olympic Games eighteen times, followed by the Soviet Union with six overall victories (not including the 1992 first-place finish by the Unified Team). In the Winter Olympic Games, Norway has placed first overall nine times and the Soviet Union (prior to its dissolution in 1991) had placed first seven times.
Summer Olympics
List by Games
List by team
Winter Olympics
List by Games

2004 Summer Olympics medal table

The 2004 Summer Olympics, officially known as the Games of the XXVIII Olympiad, were a summer multi-sport event held in Athens, the capital city of Greece, from 13 to 29 August 2004. A total of 10,625 athletes from 201 countries represented by National Olympic Committees participated in these games, competing in 301 events in 28 sports. Kiribati and Timor Leste competed for the first time in these Olympic Games.
Athletes from 74 countries won at least one medal. The United States won the most gold medals (36), the most silver medals (40) and the most medals overall (101). China finished second on the International Olympic Committee medal table (though third in terms of total medals), the country's best performance until the 2008 Beijing Olympics, where they were hosts. Russia finished third (second in total medals), and also won the most bronze medals (38). Host nation Greece finished fifteenth, with six gold, six silver, and four bronze medals, in its best total medal haul since 1896.


Fine-tuning BERT for optimal text classification performance on multiple datasets

Title: Imbalanced Multi-label Classification for Business-related Text with Moderately Large Label Spaces

Abstract: In this study, we compared the performance of four different methods for multi label text classification using a specific imbalanced business dataset. The four methods we evaluated were fine tuned BERT, Binary Relevance, Classifier Chains, and Label Powerset. The results show that fine tuned BERT outperforms the other three methods by a significant margin, achieving high values of accuracy, F1 Score, Precision, and Recall. Binary Relevance also performs well on this dataset, while Classifier Chains and Label Powerset demonstrate relatively poor performance. These findings highlight the effectiveness of fine tuned BERT for multi label text classification tasks, and suggest that it may be a useful tool for businesses seeking to analyze complex and multifaceted texts.

Title: How to Fine-Tune BERT for Text Classification?

Abstract: Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Encoder Representations from Transformers) has achieved amazing results in many language understanding tasks. In this paper, we conduct exhaustive experiments to investigate different fine-tuning methods of BERT on text classification task and provide a general solution for BERT fine-tuning. Finally, the proposed solution obtains new state-of-the-art results on eight widely-studied text classification datasets.


Heaven in Christianity

In Christianity, heaven is traditionally the location of the throne of God and the angels of God, and in most forms of Christianity it is the abode of the righteous dead in the afterlife. In some Christian denominations it is understood as a temporary stage before the resurrection of the dead and the saints' return to the New Earth.
In the Book of Acts, the resurrected Jesus ascends to heaven where, as the Nicene Creed states, he now sits at the right hand of God and will return to earth in the Second Coming. According to Catholic, Eastern Orthodox, and Oriental Orthodox teaching, Mary, mother of Jesus, is said to have been assumed into heaven without the corruption of her earthly body; she is venerated as Queen of Heaven.
In the Christian Bible, concepts about Christian eschatology, the future "kingdom of heaven", and the resurrection of the dead are found, particularly in the book of Revelation and in 1 Corinthians 15.
Description
The Bible does not provide much information on what Heaven is supposed to be like. As a result, Christian theologians are usually not very specific when describing heaven.
The Book of Revelation states that the New Jerusalem will be transported from Heaven to Earth, rather than people from Earth going to Heaven. The description of the gates of New Jerusalem in  inspired the idea of the Pearly gates, which is the informal name for the gateway to heaven according to some Christian denominations.

Heaven

Heaven, or the heavens, is a common religious cosmological or transcendent supernatural place where beings such as deities, angels, souls, saints, or venerated ancestors are said to originate, be enthroned, or reside. According to the beliefs of some religions, heavenly beings can descend to Earth or incarnate and earthly beings can ascend to Heaven in the afterlife or, in exceptional cases, enter Heaven without dying.
Heaven is often described as a "highest place", the holiest place, a Paradise, in contrast to hell or the Underworld or the "low places" and universally or conditionally accessible by earthly beings according to various standards of divinity, goodness, piety, faith, or other virtues or right beliefs or simply divine will. Some believe in the possibility of a heaven on Earth in a world to come.


Which test was devised to determine whether robots can think?

Turing test

The test was introduced by Turing in his 1950 paper "Computing Machinery and Intelligence" while working at the University of Manchester. It opens with the words: "I propose to consider the question, 'Can machines think? Because "thinking" is difficult to define, Turing chooses to "replace the question by another, which is closely related to it and is expressed in relatively unambiguous words." Turing describes the new form of the problem in terms of a three-person game called the "imitation game", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: "Are there imaginable digital computers which would do well in the imitation game?" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that "machines can think".
Since Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a "mind", "understanding", or "consciousness", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.
History

Turing test

The Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic).


UEFA European Championship

The UEFA European Football Championship, less formally the European Championship and informally the Euro or Euros, is the primary association football tournament organised by the Union of European Football Associations (UEFA). The competition is contested by UEFA members' senior men's national teams, determining the continental champion of Europe. It is the second-most watched football tournament in the world after the FIFA World Cup; the Euro 2016 final was watched by a global audience of around 600 million. The competition has been held every four years since 1960, except for 2020, when it was postponed until 2021 due to the COVID-19 pandemic in Europe, but kept the name Euro 2020. Scheduled to be in the even-numbered year between FIFA World Cup tournaments, it was originally called the European Nations' Cup before changing to its current name in 1968. Since 1996, the individual events have been branded as "UEFA Euro [year]".
Before entering the tournament, all teams other than the host nations (which qualify automatically) compete in a qualifying process. Until 2016, the championship winners could compete in the following year's FIFA Confederations Cup, but were not obliged to do so. From the 2020 edition onwards, the winner competes in the CONMEBOL–UEFA Cup of Champions.
The seventeen European Championship tournaments have been won by ten national teams: Spain have won four titles, Germany have won three titles, Italy and France have won two titles, and the Soviet Union, Czechoslovakia, the Netherlands, Denmark, Greece and Portugal have won one title each. To date, Spain is the only team to have won consecutive titles, doing so in 2008 and 2012.
The most recent championship, held in Germany in 2024, was won by Spain, who lifted a record fourth European title after beating England 2–1 in the final at Olympiastadion in Berlin.
History

UEFA Euro 2016

The 2016 UEFA European Football Championship, commonly referred to as UEFA Euro 2016 (stylised as UEFA EURO 2016) or simply Euro 2016, was the 15th UEFA European Championship, the quadrennial international men's football championship of Europe organised by UEFA. It was held in France from 10 June to 10 July 2016. Spain were the two-time defending champions, having won the 2008 and 2012 tournaments, but were eliminated in the round of 16 2-0 by Italy. Portugal won the tournament for the first time, following a 1–0 victory after extra time over the host team, France, in the final played at the Stade de France.


2010 Ukrainian presidential election

On 21 December 2009, the Central Election Commission of Ukraine formed 113 foreign polling stations.
Voters are permitted to vote at home during the presidential election.
Costs
The Central Election Commission has estimated the budget of the holding of regular presidential elections in Ukraine at ₴1.5 billion, (approximately 200 million US dollars) with additional costs required by candidates to fund their campaigns.
Each candidate is required to pay an election deposit of ₴2.5 million (Approximately US$300,000) The deposit will be refunded to the two highest polling candidates who progress to the second round of elections.
On 26 November, the Central Election Commission stated a total of ₴1.314 billion is required to hold the presidential election, including 192.2 million in 2009 and 1.122 billion in 2010.
Assessments by political analysts show that each presidential candidate will have to spend at least US$150–200mn to promote himself; this includes buying story lines in the media, visual advertising, canvassing, printing political material and, work with electoral commissions.
Chairman of the Committee of Voters of Ukraine, Oleksandr Chernenko, also commented that presidential candidates will spend 1 billion US dollars on the election campaign
The cost of the run-off ballot is estimated to be US$119 million

2024 United States presidential election

The 2024 United States presidential election will be the 60th quadrennial presidential election, set to be held on Tuesday, November 5, 2024. Voters will elect a president and vice president for a term of four years. The incumbent president, Joe Biden, a member of the Democratic Party, is running for re-election. His predecessor, Donald Trump, a member of the Republican Party, is running for re-election for a second, non-consecutive term, after losing to Biden in the 2020 presidential election. If both are nominated, this will mark the seventh presidential election rematch, and the first since 1956. In the run-up to the election, on May 30, 2024, Trump was convicted of 34 felonies related to falsifying business records, becoming the first president to be found guilty of a crime. On July 13, Trump was shot in an assassination attempt at one of his rallies.
The winner of this election is scheduled to be inaugurated on January 20, 2025. The presidential election will take place at the same time as elections for the U.S. Senate, House, gubernatorial, and state legislatures. Biden and Trump secured a majority of delegates for the Democratic and Republican parties, respectively, on March 12, though they have yet to be confirmed at their nominating conventions. Robert F. Kennedy Jr. emerged as the highest-polling third-party presidential candidate since Ross Perot in the 1992 election, running as an independent.
Leading campaign issues are expected to be abortion, border security and immigration, healthcare, education, the economy, foreign policy, LGBT rights, climate change, and democracy.
Background


Interpretable feature extraction in language models using autoencoders.

Title: Sparse Autoencoders Find Highly Interpretable Features in Language Models

Abstract: One of the roadblocks to a better understanding of neural networks' internals is \textit{polysemanticity}, where neurons appear to activate in multiple, semantically distinct contexts. Polysemanticity prevents us from identifying concise, human-understandable explanations for what neural networks are doing internally. One hypothesised cause of polysemanticity is \textit{superposition}, where neural networks represent more features than they have neurons by assigning features to an overcomplete set of directions in activation space, rather than to individual neurons. Here, we attempt to identify those directions, using sparse autoencoders to reconstruct the internal activations of a language model. These autoencoders learn sets of sparsely activating features that are more interpretable and monosemantic than directions identified by alternative approaches, where interpretability is measured by automated methods. Moreover, we show that with our learned set of features, we can pinpoint the features that are causally responsible for counterfactual behaviour on the indirect object identification task \citep{wang2022interpretability} to a finer degree than previous decompositions. This work indicates that it is possible to resolve superposition in language models using a scalable, unsupervised method. Our method may serve as a foundation for future mechanistic interpretability work, which we hope will enable greater model transparency and steerability.

Title: Scaling and evaluating sparse autoencoders

Abstract: Sparse autoencoders provide a promising unsupervised approach for extracting interpretable features from a language model by reconstructing activations from a sparse bottleneck layer. Since language models learn many concepts, autoencoders need to be very large to recover all relevant features. However, studying the properties of autoencoder scaling is difficult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents. We propose using k-sparse autoencoders [Makhzani and Frey, 2013] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier. Additionally, we find modifications that result in few dead latents, even at the largest scales we tried. Using these techniques, we find clean scaling laws with respect to autoencoder size and sparsity. We also introduce several new metrics for evaluating feature quality based on the recovery of hypothesized features, the explainability of activation patterns, and the sparsity of downstream effects. These metrics all generally improve with autoencoder size. To demonstrate the scalability of our approach, we train a 16 million latent autoencoder on GPT-4 activations for 40 billion tokens. We release training code and autoencoders for open-source models, as well as a visualizer.


Ell Stackexchange

Q: What is this food called in English? I think technically it's a fruit although they strongly resemble a vegetable:

What do you call these?
Personally, I know them as ‘paprika’.
Others say these are ‘bell peppers’, and only when ground into spice it's called paprika.
Besides grinding them into spice, you can also make soup of it or sauce. Or put slices of this on a pizza. I would call that paprika soup, or paprika sauce, or paprika slices.
Is this perhaps a difference between British vs American vs 'international' English?
P.S. regarding the fruit vs vegetable issue: not really related to the question here but I think from a botanical point of view it's a fruit, and from a culinary point of view it's a vegetable.

A: As well as being called bell peppers in the other answers, they are sometimes also called Capsicum  in some English speaking countries, as per Wikipedia Bell pepper. Capsicum is actually the Genus name of the plant.

English Stackexchange

Q: How to name a part of a piechart Which term best suits to describe a part/slice/share/portion of a piechart, disregarding what the chart is about ?

A: Since each "part" is a sector of a circle, sector can be used IMO.


A system integrating BERT and IR techniques to enhance open-domain QA performance on Wikipedia.

Title: End-to-End Open-Domain Question Answering with BERTserini

Abstract: We demonstrate an end-to-end question answering system that integrates BERT with the open-source Anserini information retrieval toolkit. In contrast to most question answering and reading comprehension models today, which operate over small amounts of input text, our system integrates best practices from IR with a BERT-based reader to identify answers from a large corpus of Wikipedia articles in an end-to-end fashion. We report large improvements over previous results on a standard benchmark test collection, showing that fine-tuning pretrained BERT with SQuAD is sufficient to achieve high accuracy in identifying answer spans.

Title: Detection of the Geminga pulsar with MAGIC hints at a power-law tail emission beyond 15 GeV

Abstract: We report the detection of pulsed gamma-ray emission from the Geminga pulsar (PSR J0633+1746) between $15\,$GeV and $75\,$GeV. This is the first time a middle-aged pulsar has been detected up to these energies. Observations were carried out with the MAGIC telescopes between 2017 and 2019 using the low-energy threshold Sum-Trigger-II system. After quality selection cuts, $\sim 80\,$hours of observational data were used for this analysis. To compare with the emission at lower energies below the sensitivity range of MAGIC, $11$ years of Fermi-LAT data above $100\,$MeV were also analysed. From the two pulses per rotation seen by Fermi-LAT, only the second one, P2, is detected in the MAGIC energy range, with a significance of $6.3\,\sigma$. The spectrum measured by MAGIC is well-represented by a simple power law of spectral index $\Gamma= 5.62\pm0.54$, which smoothly extends the Fermi-LAT spectrum. A joint fit to MAGIC and Fermi-LAT data rules out the existence of a sub-exponential cut-off in the combined energy range at the $3.6\,\sigma$ significance level. The power-law tail emission detected by MAGIC is interpreted as the transition from curvature radiation to Inverse Compton Scattering of particles accelerated in the northern outer gap.


Chinese text encoder with n-gram representations achieving state-of-the-art performance.

Title: Is Nothing Sacred? Vacuum Energy, Supersymmetry and Lorentz Breaking from Recoiling D branes

Abstract: Classical superstring vacua have zero vacuum energy and are supersymmetric and Lorentz-invariant. We argue that all these properties may be destroyed when quantum aspects of the interactions between particles and non-perturbative vacuum fluctuations are considered. A toy calculation of string/D-brane interactions using a world-sheet approach indicates that quantum recoil effects - reflecting the gravitational back-reaction on space-time foam due to the propagation of energetic particles - induce non-zero vacuum energy that is linked to supersymmetry breaking and breaks Lorentz invariance. This model of space-time foam also suggests the appearance of microscopic event horizons.

Title: Character-level Chinese-English Translation through ASCII Encoding

Abstract: Character-level Neural Machine Translation (NMT) models have recently achieved impressive results on many language pairs. They mainly do well for Indo-European language pairs, where the languages share the same writing system. However, for translating between Chinese and English, the gap between the two different writing systems poses a major challenge because of a lack of systematic correspondence between the individual linguistic units. In this paper, we enable character-level NMT for Chinese, by breaking down Chinese characters into linguistic units similar to that of Indo-European languages. We use the Wubi encoding scheme, which preserves the original shape and semantic information of the characters, while also being reversible. We show promising results from training Wubi-based models on the character- and subword-level with recurrent as well as convolutional models.


what type of data is provided in a knowledge base

W. Edwards Deming

Deming continued to advise businesses large and small. From 1985 through 1989, Deming served as a consultant to Vernay Laboratories, a rubber manufacturing firm in Yellow Springs, Ohio, with fewer than 1,000 employees. He held several week-long seminars for employees and suppliers of the small company where his famous example "Workers on the Red Beads" spurred several major changes in Vernay's manufacturing processes.
Deming joined the Graduate School of Business at Columbia University in 1988. In 1990, during his last year, he founded the W. Edwards Deming Center for Quality, Productivity, and Competitiveness at Columbia Business School to promote operational excellence in business through the development of research, best practices and strategic planning.
In 1990, Marshall Industries (NYSE:MI, 1984–1999) CEO Robert Rodin trained with the then 90-year-old Deming and his colleague Nida Backaitis. Marshall Industries' dramatic transformation and growth from $400 million to $1.8 billion in sales was chronicled in Deming's last book The New Economics, a Harvard Case Study, and Rodin's book, Free, Perfect and Now.
In 1993, Deming published his final book, The New Economics for Industry, Government, Education, which included the System of Profound Knowledge and the 14 Points for Management. It also contained educational concepts involving group-based teaching without grades, as well as management without individual merit or performance reviews.
Academic contributions
The philosophy of W. Edwards Deming has been summarized as follows:
The Deming System of Profound Knowledge

Knowledge base

In computer science, a knowledge base (KB) is a set of sentences, each sentence given in a knowledge representation language, with interfaces to tell new sentences and to ask questions about what is known, where either of these interfaces might use inference. It is a technology used to store complex structured data used by a computer system. The initial use of the term was in connection with expert systems, which were the first knowledge-based systems.
Original usage of the term


how to change share name in windows server 2008?

Superuser Stackexchange

Q: How to change the administrator account name from command prompt in Windows Server 2008 Server Core? I'm trying to change the administrator account name on my virtual machine for lab work purposes.  I'm running Windows Server 2008 Server Core, therefore I'm only using the standard command prompts.
How can I do this?

A: If it domain user install Active Directory Administration module for PowerShell.
command line:
powershell Import-module ActiveDirectory

rename domain user -
Powershell:
Get-ADUser -Identity 'Administrator' | Rename-ADObject -NewName 'Partisan'

command line:
powershell Get-ADUser -Identity 'Administrator' ^| Rename-ADObject -NewName 'Partisan'

var 2:
dsquery user -name Administrator | dsmove -newname "Partisan"

local administrator -
Powershell:
Rename-LocalUser -UserName 'Administrator' -NewUserName 'Partisan'

command line:
powershell Rename-LocalUser -UserName 'Administrator' -NewUserName 'Partisan'

var2:
wmic UserAccount where Name="Administrator" call Rename Name="Partisan"

Serverfault Stackexchange

Q: How do you configure a \\domain\sharedFolder in windows 2008? Background:

*

*I've got several Windows 2008 servers setup by someone else.

*They are all on one domain, lets call this mydomain

*Several boxes are accessing a shared folder by going to \\mydomain\share1

*I found the actual files backing the share on box1

*I need to move the location of this share to a larger drive for space reasons

I'm familiar with setting up and managing shares like \\box1\share1, but where do you manage/configure shares that are domain-wide; accessed like \\mydomain\share1 ?

A: It sounds like you have a domain based DFS configured, which is managed by the DFS Management utility.


How can a small visual-linguistic model be effectively trained using knowledge distillation?

Title: Compressing Visual-linguistic Model via Knowledge Distillation

Abstract: Despite exciting progress in pre-training for visual-linguistic (VL) representations, very few aspire to a small VL model. In this paper, we study knowledge distillation (KD) to effectively compress a transformer-based large VL model into a small VL model. The major challenge arises from the inconsistent regional visual tokens extracted from different detectors of Teacher and Student, resulting in the misalignment of hidden representations and attention distributions. To address the problem, we retrain and adapt the Teacher by using the same region proposals from Student's detector while the features are from Teacher's own object detector. With aligned network inputs, the adapted Teacher is capable of transferring the knowledge through the intermediate representations. Specifically, we use the mean square error loss to mimic the attention distribution inside the transformer block and present a token-wise noise contrastive loss to align the hidden state by contrasting with negative representations stored in a sample queue. To this end, we show that our proposed distillation significantly improves the performance of small VL models on image captioning and visual question answering tasks. It reaches 120.8 in CIDEr score on COCO captioning, an improvement of 5.1 over its non-distilled counterpart; and an accuracy of 69.8 on VQA 2.0, a 0.8 gain from the baseline. Our extensive experiments and ablations confirm the effectiveness of VL distillation in both pre-training and fine-tuning stages.


How can in-context learning emerge from pretraining documents with long-range coherence?

Title: Order Preservation in Limit Algebras

Abstract: The matrix units of a digraph algebra, A, induce a relation, known as the diagonal order, on the projections in a masa in the algebra. Normalizing partial isometries in A act on these projections by conjugation; they are said to be order preserving when they respect the diagonal order. Order preserving embeddings, in turn, are those embeddings which carry order preserving normalizers to order preserving normalizers. This paper studies operator algebras which are direct limits of finite dimensional algebras with order preserving embeddings. We give a complete classification of direct limits of full triangular matrix algebras with order preserving embeddings. We also investigate the problem of characterizing algebras with order preserving embeddings.

Title: An Explanation of In-context Learning as Implicit Bayesian Inference

Abstract: Large language models (LMs) such as GPT-3 have the surprising ability to do in-context learning, where the model learns to do a downstream task simply by conditioning on a prompt consisting of input-output examples. The LM learns from these examples without being explicitly pretrained to learn. Thus, it is unclear what enables in-context learning. In this paper, we study how in-context learning can emerge when pretraining documents have long-range coherence. Here, the LM must infer a latent document-level concept to generate coherent next tokens during pretraining. At test time, in-context learning occurs when the LM also infers a shared latent concept between examples in a prompt. We prove when this occurs despite a distribution mismatch between prompts and pretraining data in a setting where the pretraining distribution is a mixture of HMMs. In contrast to messy large-scale datasets used to train LMs capable of in-context learning, we generate a small-scale synthetic dataset (GINC) where Transformers and LSTMs both exhibit in-context learning. Beyond the theory, experiments on GINC exhibit large-scale real-world phenomena including improved in-context performance with model scaling (despite the same pretraining loss), sensitivity to example order, and instances where zero-shot is better than few-shot in-context learning.


What's the purpose of `if __name__ == '__main__'`?

Stackoverflow Stackexchange

Q: Why does it do this ? if - __name__ == '__main__'
Duplicate of:
What does if __name__== "__main__" do?

Consider this code:
if __name__ == '__main__':
    import pdb
    pdb.run("interact()\n")

What does the following line mean?
if(__name__=='__main__')

I fainted.

A: That is a check to see if you are directly running the script or if it is included in a library.
When you run a python script like this:
python myScript.py

It sends a parameter, telling you to run the programs first method, which is widely called "main", so when __name__ is __main__ you know that the program was executed from a command line or double clicked.

Stackoverflow Stackexchange

Q: html dangerous tags to avoid while developing a chat application I am developing a chat application using PHP and jQuery... all messages sent by the chat pair are appended to a <div>. While doing this, I found that tags such as <script> can be entered by users. What tags should I avoid while appending the user's message?

A: Avoid tags that will inject content into the page:

*

*applet

*script

*style

*link

*iframe

Other tags may be necessary to remove, as well. Maybe you don't want to embed images. These are things that you will need to determine. At the bare minimum, allow <p> and <br />.


is harvard kennedy school part of harvard university

Harvard Kennedy School

HKS courses Harvard Kennedy School maintains six academic divisions each headed by a faculty chair. In addition to offerings in the Harvard Kennedy School course listing, students are eligible to cross-register for courses at the other graduate and professional schools at Harvard and at the MIT Sloan School of Management, The Fletcher School of Law and Diplomacy at Tufts University, and the MIT School of Architecture and Planning. MPP coursework is focused on one of five areas, called a Policy Area of Concentration (PAC), and includes a year-long research seminar in their second year, which includes a master's thesis called a Policy Analysis Exercise.
Rankings
Harvard Kennedy School has routinely ranked as the best, or among the best, of the world's public policy graduate schools. U.S. News & World Report ranks it the best graduate school for social policy, the best for health policy, and second best for public policy analysis. In 2015 rankings, Kennedy School is ranked first in the subcategory of health policy and second in the category of public policy analysis and social policy.
Kennedy's School's foreign affairs programs have consistently ranked at the top or near the top of Foreign Policy magazine's Inside the Ivory Tower survey, which lists the world's top twenty academic international relations programs at the undergraduate, Master's, and Ph.D. levels. In 2012, for example, the survey ranked Kennedy School first overall for doctoral and undergraduate programs and third overall in the Master's category.
Student organizations
Harvard Kennedy School maintains a range of student activities, including interest-driven student caucuses, the student government (Kennedy School Student Government, known as KSSG), student-edited policy journals including Harvard Journal of Hispanic Policy, Kennedy School Review, the Journal of Middle Eastern Politics and Policy, a student newspaper (The Citizen), and a number of student athletic groups.

Harvard Kennedy School

Ira Jackson (MPA '86), dean, Peter F. Drucker and Masatoshi Ito Graduate School of Management at Claremont Graduate University
Nancy Koehn (MPP), author, historian, and Harvard Business School business history professor
Mark Lilla (MPP '80), professor of humanities, Columbia University
Hollis Robbins (MPP '90), dean of humanities, University of Utah
Mark Schuster (MPP '88), dean and founding CEO, Kaiser Permanente Bernard J. Tyson School of Medicine
Stephen Joel Trachtenberg (MPA '66), former president, George Washington University
William E. Trueheart (MPA '73), former president, Bryant University
Jonathan Zittrain (MPA '95), professor of international law, Harvard Law School and co-founder of Harvard's Berkman Klein Center for Internet & Society
Arts
Will Butler (MPA '17), musician and former member of Arcade Fire
Ashley Judd (MPA '10), actress and activist
Hill Harper (MPA '92), actor and author
Thor Steingraber (MPA '09), opera director
Damian Woetzel (MPA '07), former principal dancer, New York City Ballet
Business
Rune Bjerke (MPA '97), CEO, DNB ASA
Gregory C. Carr (MPP '86), founder, Boston Technology
Leonard S. Coleman Jr. (MPA '75), former president of the National League
Benjamin Fernandes (Exec. Ed'17), Tanzanian entrepreneur
Debra L. Lee (MPP '80), President and CEO, Black Entertainment Television
Daniel Mudd (MPA '86), former president and CEO of U.S. Fannie Mae


Stackoverflow Stackexchange

Q: printf format for unsigned __int64 on Windows I need to print a ULONGLONG value (unsigned __int64). What format should i use in printf ?
I found %llu in another question but they say it is for linux only.
Thanks for your help.

A: I recommend you use PRIu64 format specified from a standard C library. It was designed to provide users with a format specifier for unsigned 64-bit integer across different architectures.
Here is an example (in C, not C++):
#include <stdint.h>   /* For uint64_t */
#include <inttypes.h> /* For PRIu64 */
#include <stdio.h>    /* For printf */
#include <stdlib.h>   /* For exit status */

int main()
{
    uint64_t n = 1986;
    printf("And the winning number is.... %" PRIu64 "!\n", n);
    return EXIT_SUCCESS;
}

English Stackexchange

Q: Is there a verb form of the word 'surgery'? Surgery as a noun is also performance of a surgical procedure. Surgical is an adjective. I couldn't find anywhere a verb for surgery. Like how you 'operate' for the noun operation.

A: I would have said performing surgery.