DataHammer/scimrc huggingface dataset:
Question: Where are Vermeer's paintings set
Evidence: Johannes Vermeer (/vərˈmɪər, vərˈmɛər/ vər-MEER, vər-MAIR, Dutch: [vərˈmeːr], see below; also known as Jan Vermeer; October 1632 – 15 December 1675) was a Dutch Baroque Period painter who specialized in domestic interior scenes of middle-class life. During his lifetime, he was a moderately successful provincial genre painter, recognized in Delft and The Hague. Nonetheless, he produced relatively few paintings and evidently was not wealthy, leaving his wife and children in debt at his death.

Vermeer worked slowly and with great care, and frequently used very expensive pigments. He is particularly renowned for his masterly treatment and use of light in his work.

"Almost all his paintings", Hans Koningsberger wrote, "are apparently set in two smallish rooms in his house in Delft; they show the same furniture and decorations in various arrangements and they often portray the same people, mostly women."

His modest celebrity gave way to obscurity after his death. He was barely mentioned in Arnold Houbraken's major source book on 17th-century Dutch painting (Grand Theatre of Dutch Painters and Women Artists) and was thus omitted from subsequent surveys of Dutch art for nearly two centuries. In the 19th century, Vermeer was rediscovered by Gustav Friedrich Waagen and Théophile Thoré-Bürger, who published an essay attributing 66 pictures to him, although only 34 paintings are universally attributed to him today. Since that time, Vermeer's reputation has grown, and he is now acknowledged as one of the greatest painters of the Dutch Golden Age.

Similar to other major Dutch Golden Age artists such as Frans Hals and Rembrandt, Vermeer never went abroad. Also, like Rembrandt, he was an avid art collector and dealer.
Answer: Most paintings by Johannes Vermeer are set in Delft in the Netherlands.
Question: How many cents do I have if I have 3 dimes?
Answer: If you have 3 dimes, you have 30 cents.
Question: Which tournaments did Raymond Charles Haoda play in? List them in a comma separated format.
Evidence: Raymond Charles Haoda (born 15 December 1991) is a Papua New Guinean cricketer. Haoda is a right-handed batsman who bowls right-arm medium-fast. He was born in Port Moresby.

Having played age group cricket for Papua New Guinea Under-19s in the 2010 Under-19 World Cup, in which he emerged as the leading wicket taker with 15, he proceeded to be selected as a part of the Papua New Guinea squad for the 2011 World Cricket League Division Three, but did not feature. His World Cricket League debut for Papua New Guinea came in the 2011 World Cricket League Division Two. It was in this tournament that he made his List A debut against Namibia. He played a further 2 List A matches in the competition, both against Hong Kong. In his 3 matches, he took 6 wickets at a bowling average of 17.66, with best figures of 3/40.
Answer: 2010 Under-19 World Cup,  2011 World Cricket League Division Two
Question: In this exploratory work, their tests considered only documents in what corpus?
Evidence: In this first exploratory work, our tests considered only documents in French from the Annodis corpus. Annodis (ANNOtation DIScursive) is a set of documents in French that were manually enriched with notes of discursive structures. Its main characteristics are:
Two annotations: Rhetorical relations and multilevel structures.
Documents (687 000 words) taken from four sources: the Est Républicain newspaper (39 articles, 10 000 words); Wikipedia (30 articles + 30 summaries, 242 000 words); Proceedings of the conference Traitement Automatique des Langues Naturelles (TALN) 2008 (25 articles, 169 000 words); Reports from Institut Français de Relations Internationales (32 raports, 266 000 words).
The corpora were noted using Glozz.
Annodis aims at building an annotated corpus. The proposed annotations are on two levels of analysis, that is, two perspectives:
Ascendant: part of EDU are used in the construction of more complex structures, through the relations of discourse;
Descending: approaches the text in its entirety and relies on the various shallow indices to identify high-level discursive structures (macro structures).
Two types of persons annotated Annodis: linguistic experts and students. The first group constituted a $E$ subcorpus called “specialist” and the second group resulted in a $N$ subcorpus called “naive”. These rhetorically annotated subcorps were used as references in our experiments. (c.f. §SECREF6).
In this exploratory work, our tests considered only documents in French from the ANNODIS3 corpus [1]. Annodis (ANNOtation DIScursive) is a set of documents in French that were manually enriched with notes of discursive structures. Its main characteristics are:• Two annotations: Rhetorical relations 4 and multilevel structures.
Answer: ANNODIS corpus.
Question: what are the benefits of reading to your children?
Answer: Reading to your kids benefits both the kids and yourself! Children learn empathy, expand their vocabulary, become better listeners develop their imagination by listening to the stories and creating their own vision in their minds. They also develop new skills based on the content of the books. For example, they will learn how the human body works if you read them books about body parts; they will understand emotions better if you introduce them to cute picture books on feelings.  A good children's story should have repetition, relatable characters, and predictability. The story of Goldilocks, the three little pigs, Jack and the beanstalk, and Charlie and the chocolate factory are some good examples. If kids can recite the stories or rephrase them to others, they have learned to express themselves!
Question: How can teachers address health disparities in society?
Answer: The teacher works with children in the positive schools center and promotes positive, supportive and mindful learning communities where students and school staff can connect, develop and grow. Connect with each other in a safe and positive environment in a mindful manner. Positively Develop as students and staff (academically, professionally, socially, and emotionally). Grow as student scholars and educational leaders. Adults and students create a culture of respect and support.
Question: Should generative AI be banned from the classroom ?
Answer: There is no unanimous answer to the this question. Certain people believe generative AI  should be banned from the classroom, while other see this as an opportunity to ask what is the place of AI in teaching. If there is still the need for students to perform their own research and train to have the appropriate approach, using modern tool in a classroom is a must to prepare the student for a world where the use of AI will most-likely disrupt the workplace. Failing to be a universal tool, AI has contributed to open the debate on the place of AI in the classroom. Using the debate to understand how AI works can contribute to providing the student with a better understanding of AI, therefore better preparing them for the changes ahead.
Question: Is homekit's are necessary for a home?
Answer: It's really not necessary now. But good to have.
Question: Choose the word which is different from the rest: Emu, Ostrich, Kiwi, Eagle
Answer: The listed words include types of birds that can be found in different parts of the world.  The answer to the question is Eagle as the rest of the birds listed are all flightless birds
Question: Among these, which is not an Indian drink: Thandai, Barfi, Sharbat, and Lassi?
Answer: Barfi is a famous Indian sweet made of wheat flour or whole gram powder.
Question: Which programming languages did Netscape try to add to their browser in order to make it more dynamic?
Evidence: The first popular web browser with a graphical user interface, Mosaic, was released in 1993. Accessible to non-technical people, it played a prominent role in the rapid growth of the nascent World Wide Web. The lead developers of Mosaic then founded the Netscape corporation, which released a more polished browser, Netscape Navigator, in 1994. This quickly became the most-used.

During these formative years of the Web, web pages could only be static, lacking the capability for dynamic behavior after the page was loaded in the browser. There was a desire in the flourishing web development scene to remove this limitation, so in 1995, Netscape decided to add a scripting language to Navigator. They pursued two routes to achieve this: collaborating with Sun Microsystems to embed the Java programming language, while also hiring Brendan Eich to embed the Scheme language.
Answer: The programming languages Netscape tried to add to their browser to make it more dynamic were Java and Scheme.

In 1995, Netscape pursued two routes to add dynamic behavior to their browser, Netscape Navigator:
1. Collaborating with Sun Microsystems to embed the Java programming language
2. Hiring Brendan Eich to embed the Scheme language
Question: Classify each of the following music genres as broadly popular or niche: Rock, Hip Hop, Pop,   Heavy Metal, Bluegrass, Country
Answer: Broadly Popular: Rock, Hip Hop, Pop. Niche: Heavy Metal, Bluegrass, Country.
Question: my health insurance covers preventative procedures like vaccines 100%. But I know some health care providers charge a "visit fee" in addition to the vaccine fee. What's the difference between getting a vaccine in a clinic and a pharmacy? Will getting a vaccine in a clinic (such as minute clinics) result in charges?
Answer: The main difference between getting a vaccine at a clinic and a pharmacy comes down to the setting and the potential fees involved.

Setting:
A clinic, such as a minute clinic, is a small healthcare facility that provides basic medical services, including vaccinations. It is typically staffed by nurse practitioners or physician assistants, and may be affiliated with a larger medical institution. Minute clinics are often located in retail stores, like pharmacies or supermarkets.
A pharmacy, on the other hand, is a place where medications are dispensed and can also offer vaccination services. Many pharmacies have pharmacists who are trained to administer vaccines.

Fees:
Both clinics and pharmacies may charge a fee for administering a vaccine, but these fees can vary. In general, the vaccine fee covers the cost of the vaccine itself, while the visit fee or administration fee covers the cost of having a healthcare professional administer the vaccine.
When it comes to health insurance coverage, many plans cover preventive procedures like vaccines at 100%. However, the coverage of visit fees or administration fees can vary depending on your insurance plan and the location where you receive the vaccine.

To avoid unexpected charges, it's important to verify the following information:

Check with your insurance provider to confirm what is covered and if there are any out-of-pocket expenses.
Inquire about potential fees at the clinic or pharmacy before scheduling your vaccine appointment. This includes asking about both the vaccine fee and any visit or administration fees.
Ultimately, whether you choose to get a vaccine at a clinic or a pharmacy, it's essential to understand the potential fees involved and how your insurance coverage applies to those fees.
Question: How old is the Greek language?
Answer: The Greek language is the oldest language in Europe, spoken since 1450 years before Christ. Currently Greek is spoken in Greece, Albania and Cyprus. About 13 million people still speak Greek today and many in the greater diaspora.
Question: From the passage identify the different types of mortality encompassed by Child mortality. Display the results in a comma separated format.
Evidence: Child mortality is the mortality of children under the age of five. The child mortality rate, also under-five mortality rate, refers to the probability of dying between birth and exactly five years of age expressed per 1,000 live births.It encompasses neonatal mortality and infant mortality (the probability of death in the first year of life).Reduction of child mortality is reflected in several of the United Nations' Sustainable Development Goals. Target 3.2 is "by 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce … under‑5 mortality to at least as low as 25 per 1,000 live births."Child mortality rates have decreased in the last 40 years. While in 1990, 12.6 million children under age five died, in 2016 that number fell to 5.6 million children, and then in 2020, the global number fell again to 5 million. Rapid progress has resulted in a significant decline in preventable child deaths since 1990, with the global under-5 mortality rate declining by over half between 1990 and 2016. While in 1990, 12.6 million children under age five died, in 2016 that number fell to 5.6 million children. However, despite advances, there are still 15,000 under-five deaths per day from largely preventable causes. About 80 per cent of these occur in sub-Saharan Africa and South Asia, and just 6 countries account for half of all under-five deaths: China, India, Pakistan, Nigeria, Ethiopia and the Democratic Republic of the Congo. 45% of these children died during the first 28 days of life. Death rates were highest among children under age 1, followed by children ages 15 to 19, 1 to 4, and 5 to 14.
Answer: neonatal mortality, infant mortality
Question: Given a reference text about Francoist Spain, when did Franco rule Spain and when did he die?
Evidence: Francoist Spain (Spanish: España franquista), or the Francoist dictatorship (dictadura franquista), was the period of Spanish history between 1939 and 1975, when Francisco Franco ruled Spain after the Spanish Civil War with the title Caudillo. After his death in 1975, Spain transitioned into a democracy. During this time period, Spain was officially known as the Spanish State (Estado Español).

The nature of the regime evolved and changed during its existence. Months after the start of the Spanish Civil War in July 1936, Franco emerged as the dominant rebel military leader and was proclaimed head of state on 1 October 1936, ruling a dictatorship over the territory controlled by the Nationalist faction. The 1937 Unification Decree, which merged all parties supporting the rebel side, led to Nationalist Spain becoming a single-party regime under the FET y de las JONS. The end of the war in 1939 brought the extension of the Franco rule to the whole country and the exile of Republican institutions. The Francoist dictatorship originally took a form described as "fascistized dictatorship", or "semi-fascist regime", showing clear influence of fascism in fields such as labor relations, the autarkic economic policy, aesthetics, and the single-party system. As time went on, the regime opened up and became closer to developmental dictatorships, although it always preserved residual fascist elements.
Answer: Francisco Franco ruled Spain from 1935 until he died in 1975.
Question: What was the best performing baseline?
Evidence: We compared several summarization methods which can be categorized into three groups: unsupervised, non-neural supervised, and neural supervised methods. For the unsupervised methods, we tested:
As a baseline, we used Lead-N which selects INLINEFORM0 leading sentences as the summary. For all methods, we extracted 3 sentences as the summary since it is the median number of sentences in the gold summaries that we found in our exploratory analysis.
Table TABREF26 shows the test INLINEFORM0 score of ROUGE-1, ROUGE-2, and ROUGE-L of all the tested models described previously. The mean and standard deviation (bracketed) of the scores are computed over the 5 folds. We put the score obtained by an oracle summarizer as Oracle. Its summaries are obtained by using the true labels. This oracle summarizer acts as the upper bound of an extractive summarizer on our dataset. As we can see, in general, every scenario of NeuralSum consistently outperforms the other models significantly. The best scenario is NeuralSum with word embedding size of 300, although its ROUGE scores are still within one standard deviation of NeuralSum with the default word embedding size. Lead-3 baseline performs really well and outperforms almost all the other models, which is not surprising and even consistent with other work that for news summarization, Lead-N baseline is surprisingly hard to beat. Slightly lower than Lead-3 are LexRank and Bayes, but their scores are still within one standard deviation of each other so their performance are on par. This result suggests that a non-neural supervised summarizer is not better than an unsupervised one, and thus if labeled data are available, it might be best to opt for a neural summarizer right away. We also want to note that despite its high ROUGE, every NeuralSum scenario scores are still considerably lower than Oracle, hinting that it can be improved further. Moreover, initializing with FastText pre-trained embedding slightly lowers the scores, although they are still within one standard deviation. This finding suggests that the effect of FastText pre-trained embedding is unclear for our case.
We compared several summarization methods which can be categorized into three groups: unsupervised, non-neural supervised, and neural supervised methods. 
As a baseline, we used Lead-N which selects INLINEFORM0 leading sentences as the summary. For all methods, we extracted 3 sentences as the summary since it is the median number of sentences in the gold summaries that we found in our exploratory analysis.
Lead-3 baseline performs really well and outperforms almost all the other models, which is not surprising and even consistent with other work that for news summarization, Lead-N baseline is surprisingly hard to beat.
Answer: The answers are shown as follows:
* Lead-3
Question: What is the research area most closely related to the author's work?
Evidence: Our work is most closely related to two lines of research: automatic post-editing (APE) and document-level machine translation.
Our work is most closely related to two lines of research: automatic post-editing (APE) and document-level machine translation.
Answer: Automatic post-editing (APE) and document-level machine translation.
Question: Classify each of the following football positions as offense or defense: quarterback, running back, linebacker, cornerback, safety, tight end
Answer: Quarterback: Offense
Running Back: Offense
Linebacker: Defense
Cornerback: Defense
Safety: Defense
Tight End: Offense
Question: So this paper turns unstructured text inputs to parameters that GNNs can read?
Evidence: In this section, we will introduce the general framework of GP-GNNs. GP-GNNs first build a fully-connected graph $\mathcal {G} = (\mathcal {V}, \mathcal {E})$ , where $\mathcal {V}$ is the set of entities, and each edge $(v_i, v_j) \in \mathcal {E}, v_i, v_j \in \mathcal {V}$ corresponds to a sequence $s = x_0^{i,j}, x_1^{i,j}, \dots , x_{l-1}^{i,j}$ extracted from the text. After that, GP-GNNs employ three modules including (1) encoding module, (2) propagation module and (3) classification module to proceed relational reasoning, as shown in Fig. 2 .
GP-GNNs first build a fully-connected graph $\mathcal {G} = (\mathcal {V}, \mathcal {E})$ , where $\mathcal {V}$ is the set of entities, and each edge $(v_i, v_j) \in \mathcal {E}, v_i, v_j \in \mathcal {V}$ corresponds to a sequence $s = x_0^{i,j}, x_1^{i,j}, \dots , x_{l-1}^{i,j}$ extracted from the text. 
Answer: Yes.
Question: What is proof that proposed functional form approximates well generalization error in practice?
Evidence: As fig:fit shows, estimated test accuracy is highly correlated with actual test accuracy for various datasets, with worst-case values $\mu <1\%$ and $\sigma <5\%$ . Note that the number of free parameters is small ($||\le 6$) compared to the number of points (42–49 model-data configurations), demonstrating the appropriateness of the proposed function for modeling the complex error landscape.
As fig:fit shows, estimated test accuracy is highly correlated with actual test accuracy for various datasets, with worst-case values $\mu <1\%$ and $\sigma <5\%$ . Note that the number of free parameters is small ($||\le 6$) compared to the number of points (42–49 model-data configurations), demonstrating the appropriateness of the proposed function for modeling the complex error landscape.
Answer: The answers are shown as follows:
* estimated test accuracy is highly correlated with actual test accuracy for various datasets
* appropriateness of the proposed function for modeling the complex error landscape
Question: What dataset do they use?
Evidence: We use datasets from the NIST DUC-05, DUC-06 and DUC-07 shared tasks BIBREF7, BIBREF19, BIBREF20. Given a question and a cluster of newswire documents, the contestants were asked to generate a 250-word summary answering the question. DUC-05 contains 1,600 summaries (50 questions x 32 systems); in DUC-06, 1,750 summaries are included (50 questions x 35 systems); and DUC-07 has 1,440 summaries (45 questions x 32 systems).
We use datasets from the NIST DUC-05, DUC-06 and DUC-07 shared tasks BIBREF7, BIBREF19, BIBREF20. Given a question and a cluster of newswire documents, the contestants were asked to generate a 250-word summary answering the question. DUC-05 contains 1,600 summaries (50 questions x 32 systems); in DUC-06, 1,750 summaries are included (50 questions x 35 systems); and DUC-07 has 1,440 summaries (45 questions x 32 systems).
Answer: The answers are shown as follows:
* datasets from the NIST DUC-05, DUC-06 and DUC-07 shared tasks
Question: However, the authors could have taken this idea a step further by considering the model’s original softmax prediction (y) as the soft pseudo label and maximizing the KL divergence between y (the model’s original softmax predictions) and f(\hat{x}). Did you consider using this alternative loss function for creating adv examples?Can you justify this choice of loss function for the baseline evaluations?
Evidence: In Section 4.3, the PGD-based baselines disturb input images by maximizing the entropy loss
of source models. In Section 4.5.2, the PGD-based baselines disturb input images by maxi-mizing the cross-entropy loss of source models, where the perturbation noise is formulated as
δ = sign (∇x(i−1)CE(y,argmax(y)).argmax(y) is regarded as the pseudo label for the input.Here  Here we re-implement PGD-based baselines with KL divergence, which means the baselines disturb the input images by maximizing the KL divergence between the predict distribution y and y0, where y0 is the distribution predict for the original clean image x. The perturbation generated in each
gradient ascent step can be reformulated as δ = sign(∇x(i−1)KL(y,y0).) In this experiment, we use ResNet-18 and Cifar-10 as the source model and source dataset, and use Cifar-100 as the target
dataset. The generalized transfer attack results on MobileNet-V3, VGG-16, ResNet-18, ResNet-34, SeResNet-26, and DenseNet-16 are reported in Table 8. The comparison between the results here
and those in Table 2 demonstrate that the KL divergence cannot improve baselines.
Answer: Thanks for your suggestion. Following your suggestion, we conducted an experiment where the baselines disturb the input images by maximizing the KL divergence between y (the model’s original softmax predictions) and f(\hat{x}). In this experiment, we use ResNet-18 and Cifar-10 as the source model and source dataset, and use Cifar-100 as the target dataset. The generalized transfer attack results on MobileNet-V3, VGG-16, ResNet-18, ResNet-34, SeResNet-26, and DenseNet-16 are:
FGSM: 49.3%, 63.3%, 57.9%, 56.5%, 58.8%, and 72.5%;
PGD: 39.6%, 52.5%, 45.0%, 44.5%, 48.0%, and 62.7%;
DI: 41.2%, 54.6%, 46.5%, 45.9%, 50.0%, and 64.2%;
MI: 47.3%, 62.6%, 56.9%, 56.1%, 58.2%, and 72.4%;
TI-DIM: 50.6%, 43.1%, 45.6%, 45.9%, 46.9%, and 52.4%;
The comparison between the results here and those in Table 2 demonstrate that the KL divergence cannot improve PGD-based baselines. We will include this experiment in our Appendix (Section A.5.3).
Question: Identify which instrument is string or percussion: Caxirola, Ducheke
Answer: Ducheke is string, Caxirola is percussion.