DataHammer/scimrc huggingface dataset:
Question: Extract the sources of electricity mentioned in the text and the percentage of electricity that comes from that source in Canada and list them using bullets in the format {Source} - {Percentage}
Evidence: Forgiven,X-Men,Generation X,X-Corps,New Warriors,Patsy Walker Temp Agency
Answer: Jubilee appeared regularly as an X-Men team member in The Uncanny X-Men through issue #280 (Sept. 1991), and later was a staple of X-Men (1991) as a member of the X-Men's Blue Team. During the same timeframe, she also served as Wolverine's sidekick in Wolverine vol. 2 #40–75 (1991–1993).

After the Phalanx Covenant storyline in X-Men #16–17 and The Uncanny X-Men #316–317, Jubilee, who was then a teenager, was transferred to the X-Men trainee squad Generation X and starred in the entire run of Generation X #1–75 (1994–2001). After the dissolution of Generation X, Jubilee returned to the pages of The Uncanny X-Men, first as a member of the X-Corporation (#403–406, 2002), and later as a team member in her own right (#423–437, 2003–2004). Jubilee had a six-issue self-titled limited series in 2004 written by Robert Kirkman, but loses her mutant powers in House of M – The Day After #1 (Jan. 2006). She then adopts the alias Wondra and joins the reconstituted New Warriors in New Warriors vol. 4 #1–20 (2007–2009).

Jubilee is affected with vampirism during the "Curse of the Mutants" in X-Men vol. 3 #1 (July 2010) and remained a sporadic character on that title through issue #27 (April 2012), as well as a supporting character in X-23 vol. 3 (2010–2011). In 2011, she saw print in her second four issue limited series, Wolverine and Jubilee written by Kathryn Immonen and drawn by Phil Noto, as an aftermath follow-up to the Curse of the Mutants storyline. Jubilee later featured as a regular character in the all-female X-Men vol. 4 #1–25 (2013–2015), and as a supporting character in Patsy Walker, a.k.a. Hellcat! (2016). She returned as a main cast member in Generation X vol. 2 #1-9, #85-87 (2017-2018) as the adult mentor to the new teenage main characters, during which she was cured of vampirism and had her mutant powers restored.
Question: How to identify the informativeness of samples in the speech recognition community?
Evidence: State-of-the-art automatic speech recognition (ASR) systems BIBREF0 have large model capacities and require significant quantities of training data to generalize. Labeling thousands of hours of audio, however, is expensive and time-consuming. A natural question to ask is how to achieve better generalization with fewer training examples. Active learning studies this problem by identifying and labeling only the most informative data, potentially reducing sample complexity. How much active learning can help in large-scale, end-to-end ASR systems, however, is still an open question.
The speech recognition community has generally identified the informativeness of samples by calculating confidence scores. In particular, an utterance is considered informative if the most likely prediction has small probability BIBREF1 , or if the predictions are distributed very uniformly over the labels BIBREF2 . Though confidence-based measures work well in practice, less attention has been focused on gradient-based methods like Expected Gradient Length (EGL) BIBREF3 , where the informativeness is measured by the norm of the gradient incurred by the instance. EGL has previously been justified as intuitively measuring the expected change in a model's parameters BIBREF3 .We formalize this intuition from the perspective of asymptotic variance reduction, and experimentally, we show EGL to be superior to confidence-based methods on speech recognition tasks. Additionally, we observe that the ranking of samples scored by EGL is not correlated with that of confidence scoring, suggesting EGL identifies aspects of an instance that confidence scores cannot capture.
In BIBREF3 , EGL was applied to active learning on sequence labeling tasks, but our work is the first we know of to apply EGL to speech recognition in particular. Gradient-based methods have also found applications outside active learning. For example, BIBREF4 suggests that in stochastic gradient descent, sampling training instances with probabilities proportional to their gradient lengths can speed up convergence. From the perspective of variance reduction, this importance sampling problem shares many similarities to problems found in active learning.
The speech recognition community has generally identified the informativeness of samples by calculating confidence scores.
Answer: The speech recognition community has generally identified the informativeness of samples by calculating confidence scores.
Question: What is triangulation?
Evidence: Conclusion
Conclusion
Answer: Answer with content missing: (Chapter 3) The concept can be easily explained with an example, visualized in Figure 1. Consider the Portuguese (Pt) word trabalho which, according to the MUSE Pt–En dictionary, has the words job and work as possible En translations. In turn, these two En words can be translated to 4 and 5 Czech (Cs) words respectively. By utilizing the transitive property (which translation should exhibit) we can identify the set of 7 possible Cs translations for the Pt word trabalho.
Question: List five happy words
Answer: Elated, joyful, amused, grateful, ecstatic all evoke happiness
Question: Who is Maria Ludwiga Michalk?
Evidence: Maria Ludwiga Michalk (born 6 December 1949) is a German politician. She was a member of the German Bundestag from 1990 to 1994 and from 2002 to 2017 as a member of the Christian Democratic Union (CDU) party.

She was born in Merka in Radibor municipality and attended the local Sorbian high school. She trained as an industrial clerk and then studied business economics at a technical college.

She became a member of the East German Christian Democratic Union (CDU) in 1972. In 1990, she was named to the CDU district council for Bautzen district. In 1990, she was elected to the Volkskammer. She was subsequently elected to the Bundestag later that year. After leaving the Bundestag in 1994, she managed an education centre in Bischofswerda for seven years. In 2002, she was elected to the Bundestag again. In 2016, she announced that she would not run for reelection to the Bundestag in 2017.

Michalk was awarded the Sächsische Verfassungsmedaille and was named to the Order of Merit of the Federal Republic of Germany.
Answer: Maria Ludwiga Michalk is a German politician, serving in the German Bundestag from 1990 to 1994 and from 2002 to 2017. She is a member of the Christian Democratic Union (CDU) party.
Question: What challenges remain unresolved?
Evidence: blackIn the paper, we first proposed a taxonomy to organize GAN based text-to-image synthesis frameworks into four major groups: semantic enhancement GANs, resolution enhancement GANs, diversity enhancement GANs, and motion enhancement GANs. The taxonomy provides a clear roadmap to show the motivations, architectures, and difference of different methods, and also outlines their evolution timeline and relationships. Following the proposed taxonomy, we reviewed important features of each method and their architectures. We indicated the model definition and key contributions from some advanced GAN framworks, including StackGAN, StackGAN++, AttnGAN, DC-GAN, AC-GAN, TAC-GAN, HDGAN, Text-SeGAn, StoryGAN etc. Many of the solutions surveyed in this paper tackled the highly complex challenge of generating photo-realistic images beyond swatch size samples. In other words, beyond the work of BIBREF8 in which images were generated from text in 64$\times $64 tiny swatches. Lastly, all methods were evaluated on datasets that included birds, flowers, humans, and other miscellaneous elements. We were also able to allocate some important papers that were as impressive as the papers we finally surveyed. Though, these notable papers have yet to contribute directly or indirectly to the expansion of the vast computer vision AI field. Looking into the future, an excellent extension from the works surveyed in this paper would be to give more independence to the several learning methods (e.g. less human intervention) involved in the studies as well as increasing the size of the output images.
Looking into the future, an excellent extension from the works surveyed in this paper would be to give more independence to the several learning methods (e.g. less human intervention) involved in the studies as well as increasing the size of the output images.
Answer: The answers are shown as follows:
* give more independence to the several learning methods (e.g. less human intervention) involved in the studies
* increasing the size of the output images
Question: what are some tips to start practicing meditation?
Answer: Start short and simple with a 2 or 3 min breathing exercise every day and gradually increase your meditation duration. Join a meditation group could help you keep yourself accountable as well. Pay attention to your presence and awareness, and observe what is happening with your thoughts, without judgements. You can also seek help from technologies, mobile apps like Calm, Headspace are great tools for starters.
Question: When was the last flight of Concorde?
Answer: On 26 November 2003
Question: Will human beings be replaced by robots?
Answer: The answer is it might be possible, but hard to say. Nowadays with the fast development in artificial intelligent and robotics, there are a lot of human jobs replaced by robots already. For example, in the factories, robots have been widely used to make parts of machines, packaging product goods, or conducting accurate operations. Human has limitations in many areas, and those heavy labor jobs are usually replaced by robot machines. 

However, human beings still have very complex brain and nerve systems. AI development can try to approximate human beings, but can they ever exceed to achieve the limit? We will wait and see.
Question: What is the best place to visit in Greece
Answer: Santorini or any Greek Island
Athens
Question: Which company provided support for the study?
Evidence: We gratefully acknowledge the support of the NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
We gratefully acknowledge the support of the NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
Answer: NVIDIA Corporation.
Question: What classifier is used for emergency detection?
Evidence: The first classifier model acts as a filter for the second stage of classification. We use both SVM and NB to compare the results and choose SVM later for stage one classification model, owing to a better F-score. The training is performed on tweets labeled with classes , and based on unigrams as features. We create word vectors of strings in the tweet using a filter available in the WEKA API BIBREF9 , and perform cross validation using standard classification techniques.
Answer: The answers are shown as follows:
* SVM
Question: What specie is the Anachemmis sober?
Evidence: Anachemmis sober is a species of false wolf spiders & wandering spiders in the family Zoropsidae. It is found in the United States.
Answer: Anachemmis sober is part of the family of spiders of the species of false wolf.
Question: What is the best format for storing data in a lakehouse?
Answer: Different formats have their pros and cons in the context of storing data in a lakehouse. The three most popular formats are delta lake, iceberg and hudi. Among these three, delta lake is most widely used lakehouse storage format. Iceberg and hudi are also gaining acceptance as more and more companies are building their lakehouses. While delta lake covers most lakehouse features, if merge-on-read is an important feature for you then Hudi might be the way to go, and if partition evolution is an important feature for you then Iceberg might be the best option for you.
Question: What are some examples of fruit that are not common in American grocery stores?
Answer: Fruits that are typically not found in American grocery stores include jackfruit, longan, durian, snake fruit, lychees, rambutan, and mamey sapote.
Question: By how much do they outperform other models in the sentiment in intent classification tasks?
Evidence: Experimental results for the Twitter Sentiment Classification task on Kaggle's Sentiment140 Corpus dataset, displayed in Table TABREF37, show that our model has better F1-micros scores, outperforming the baseline models by 6$\%$ to 8$\%$. We evaluate our model and baseline models on three versions of the dataset. The first one (Inc) only considers the original data, containing naturally incorrect tweets, and achieves accuracy of 80$\%$ against BERT's 72$\%$. The second version (Corr) considers the corrected tweets, and shows higher accuracy given that it is less noisy. In that version, Stacked DeBERT achieves 82$\%$ accuracy against BERT's 76$\%$, an improvement of 6$\%$. In the last case (Inc+Corr), we consider both incorrect and correct tweets as input to the models in hopes of improving performance. However, the accuracy was similar to the first aforementioned version, 80$\%$ for our model and 74$\%$ for the second highest performing model. Since the first and last corpus gave similar performances with our model, we conclude that the Twitter dataset does not require complete sentences to be given as training input, in addition to the original naturally incorrect tweets, in order to better model the noisy sentences.
Experimental results for the Intent Classification task on the Chatbot NLU Corpus with STT error can be seen in Table TABREF40. When presented with data containing STT error, our model outperforms all baseline models in both combinations of TTS-STT: gtts-witai outperforms the second placing baseline model by 0.94% with F1-score of 97.17%, and macsay-witai outperforms the next highest achieving model by 1.89% with F1-score of 96.23%.
FLOAT SELECTED: Table 7: F1-micro scores for original sentences and sentences imbued with STT error in the Chatbot Corpus. The noise level is represented by the iBLEU score (See Eq. (5)).
Experimental results for the Twitter Sentiment Classification task on Kaggle's Sentiment140 Corpus dataset, displayed in Table TABREF37, show that our model has better F1-micros scores, outperforming the baseline models by 6$\%$ to 8$\%$. 
Experimental results for the Intent Classification task on the Chatbot NLU Corpus with STT error can be seen in Table TABREF40. When presented with data containing STT error, our model outperforms all baseline models in both combinations of TTS-STT: gtts-witai outperforms the second placing baseline model by 0.94% with F1-score of 97.17%, and macsay-witai outperforms the next highest achieving model by 1.89% with F1-score of 96.23%.
FLOAT SELECTED: Table 7: F1-micro scores for original sentences and sentences imbued with STT error in the Chatbot Corpus. The noise level is represented by the iBLEU score (See Eq. (5)).
Answer: In the sentiment classification task by 6% to 8% and in the intent classification task by 0.94% on average
Question: What is SSO?
Answer: Single sign-on (SSO) is an authentication scheme that allows a user to log in with a single ID to any of several related, yet independent, software systems. True single sign-on allows the user to log in once and access services without re-entering authentication factors.
Question: Why are plant leaves green?
Answer: Typically, plant leaves are green as they contain chlorophyll, which is a natural compound that helps the plant to absorb energy from light. 

However, leaves are not always green - some plants naturally have pink or orange leaves, and most plants also change leaf colors according to the seasons.
Question: How does the German voting system work?
Answer: Each voter has two votes. The first vote is for a direct constituency candidate. The candidate with the relative majority receives a guaranteed seat in parliament. The second vote is for a party's list of candidates, which applies at the state level. The second vote determines the overall proportional party representation in the parliament. Overhang seats might be added to satisfy the direct mandates from the first vote.
Question: What is the standard Gibbs free energy
Evidence: The standard Gibbs free energy of formation (Gf°) of a compound is the change of Gibbs free energy that accompanies the formation of 1 mole of a substance in its standard state from its constituent elements in their standard states (the most stable form of the element at 1 bar of pressure and the specified temperature, usually 298.15 K or 25 °C).

The table below lists the standard Gibbs function of formation for several elements and chemical compounds and is taken from Lange's Handbook of Chemistry. Note that all values are in kJ/mol. Far more extensive tables can be found in the CRC Handbook of Chemistry and Physics and the NIST JANAF tables. The NIST Chemistry WebBook (see link below) is an online resource that contains standard enthalpy of formation for various compounds along with the standard absolute entropy for these compounds from which the standard Gibbs free energy of formation can be calculated.
Answer: The standard Gibbs free energy of formation (Gf°) of a compound is the change of Gibbs free energy that accompanies the formation of 1 mole of a substance in its standard state from its constituent elements in their standard states (the most stable form of the element at 1 bar of pressure and the specified temperature, usually 298.15 K or 25 °C).
Question: Who is the greatest golfer of all time?
Answer: The greatest golfer of all time is a highly contested topic, although a few names come to mind for any golfer in the modern era. Many folks will look at the players who have won the most Major events, of which there are only four each year. The winningest golfer based on number of Major wins is Jack Nicklaus, and the second winningest golfer is Tiger Woods. 

Many would say that means Jack Nicklaus is the greatest golfer of all time, however greatness can also be measured by one's impact on the game. Tiger Woods opened the door for an entire generation of young players coming from diverse backgrounds. This is a fundamental change to a sport that is historically a game of privilege and aristocracy. 

I would argue that the combination of having a winning record in Major events and having a significant cultural impact on the game makes Tiger Woods the greatest and most influential golfer of all time.
Question: How better are results compared to baseline models?
Evidence: We see that parent quality is a simple yet effective feature and SVM model with this feature can achieve significantly higher ($p<0.001$) F1 score ($46.61\%$) than distance from the thesis and linguistic features. Claims with higher impact parents are more likely to be have higher impact. Similarity with the parent and thesis is not significantly better than the majority baseline. Although the BiLSTM model with attention and FastText baselines performs better than the SVM with distance from the thesis and linguistic features, it has similar performance to the parent quality baseline.
We find that the flat representation of the context achieves the highest F1 score. It may be more difficult for the models with a larger number of parameters to perform better than the flat representation since the dataset is small. We also observe that modeling 3 claims on the argument path before the target claim achieves the best F1 score ($55.98\%$).
We see that parent quality is a simple yet effective feature and SVM model with this feature can achieve significantly higher ($p<0.001$) F1 score ($46.61\%$) than distance from the thesis and linguistic features.
Although the BiLSTM model with attention and FastText baselines performs better than the SVM with distance from the thesis and linguistic features, it has similar performance to the parent quality baseline.
We find that the flat representation of the context achieves the highest F1 score. It may be more difficult for the models with a larger number of parameters to perform better than the flat representation since the dataset is small. We also observe that modeling 3 claims on the argument path before the target claim achieves the best F1 score ($55.98\%$).
Answer: F1 score of best authors' model is 55.98 compared to BiLSTM and FastText that have F1 score slighlty higher than 46.61.
Question: How to stay focused during work?
Answer: You can try below tips:
1. Have good sleep. Good sleep helps improve your concentration and memory.
2. Exercise more. Exercise helps you be more energetic.
3. Avoid multi-tasking. Multi-tasking makes us feel productive, but it can easily lead to burnout.
4. Eat well. Eat healthy food. Balanced nutrition is foundation to everything.

At last, I recommend to read a book "Deep Work" which outlines some tips of how to be more focused and productive.
Question: How authors justify that question answering dataset presented is realistic?
Evidence: CFQ contains 239,357 English question-answer pairs that are answerable using the public Freebase data. (The data URL is not yet provided for anonymous review.) We include a list of MIDs such that their English names map unambiguously to a MID. Table TABREF17(a) summarizes the overall statistics of CFQ. Table TABREF17(b) uses numbers from BIBREF8 and from an analysis of WebQuestionsSP BIBREF17 and ComplexWebQuestions BIBREF18 to compare three key statistics of CFQ to other semantic parsing datasets (none of which provide annotations of their compositional structure). CFQ contains the most query patterns by an order of magnitude and also contains significantly more queries and questions than the other datasets. Note that it would be easy to boost the raw number of questions in CFQ almost arbitrarily by repeating the same question pattern with varying entities, but we use at most one entity substitution per question pattern. Appendix SECREF10 contains more detailed analyses of the data distribution.
CFQ contains the most query patterns by an order of magnitude and also contains significantly more queries and questions than the other datasets. 
Answer: The answers are shown as follows:
* CFQ contains the most query patterns by an order of magnitude and also contains significantly more queries and questions than the other datasets
Question: How do you clear a scuba mask underwater ?
Answer: If your scuba mask fills with water while you are submerged, you can clear the water by following this simple process.

1) Inhale through your mouth
2) Push the top front edge of the mask towards your face, or lightly lift the lower section from your face
3) Exhale through your nose.

By reducing the pressure on the lower portion of the mask, and exhaling through your nose, the air added to the mask will force the water out of the bottom seal.
Question: Which of these are Aamir Khan Movies : Sarfarosh, Mela, Dil, Darr, Beta
Answer: Sarfarosh, Mela, Dil are Aamir Khan Movies. Darr had Sunny Deol and Shah Kurh Khan in the lead roles  , Beta had Anil Kapoor in the lead role.
Question: What are some easy food recipes that include beef?
Answer: Some recipes that include beef as an ingredient include beef stew, roast beef, beef wellington, and ground beef burgers.
Question: Tell me whether or not these foods are on EWG's dirty dozen list
Answer: avocados, kiwi, pairs, blueberries, green beans, kale, cabbage
Question: What is MLflow?
Answer: MLflow is an open-source framework for MLOps that automates the full production lifecycle, from model training and experimentation to model lifecycle management and deployment.