DataHammer/scimrc huggingface dataset:
Question: Why do they propose to cascade a neural network as a projection function?
Evidence: To flexibly model observed embeddings and yield a new representation space that is more suitable for the syntax model, we propose to cascade a neural network as a projection function, deterministically transforming the simple space defined by the Gaussian HMM to the observed embedding space. We denote the latent embedding of the INLINEFORM0 word in a sentence as INLINEFORM1 , and the neural projection function as INLINEFORM2 , parameterized by INLINEFORM3 . In the case of sequential Markov structure, our new model corresponds to the following generative process:

For each time step INLINEFORM0 ,

[noitemsep, leftmargin=*]
Draw the latent state INLINEFORM0 
Draw the latent embedding INLINEFORM0 
Deterministically produce embedding
 INLINEFORM0 

The graphical model is depicted in Figure FIGREF6 . The deterministic projection can also be viewed as sampling each observation from a point mass at INLINEFORM0 . The joint distribution of our model is: DISPLAYFORM0 
where INLINEFORM0 is a conditional Gaussian distribution, and INLINEFORM1 is the Dirac delta function centered at INLINEFORM2 : DISPLAYFORM0 
To flexibly model observed embeddings and yield a new representation space that is more suitable for the syntax model, we propose to cascade a neural network as a projection function, deterministically transforming the simple space defined by the Gaussian HMM to the observed embedding space.
Answer: To flexibly model observed embeddings and yield a new representation space that is more suitable for the syntax model.
Question: How do I change a light bulb?
Answer: In order to change a light bulb, you will first need to find a replacement. Make sure to disable power to the bulb before removing or installing it. You can find the size, type, and wattage written on the bulb you are trying to replace. If the information isn't on the bulb, you can take the bulb with you to the store to help you match it to a replacement. Remove the dead bulb and install the replacement. Then restore power to the bulb to test it.
Question: Who wrote the score for the film "Rise of the Planet of the Apes"?
Evidence: The score for the Rise of the Planet of the Apes film was written by Patrick Doyle and performed by the Hollywood Studio Symphony conducted by James Shearman. On being compared to the musical works of Jerry Goldsmith and Danny Elfman, who scored the previous installment, and its impact on the tonal and primitive quality, Doyle opined that the musical approach would be fresh. The main concern was to have the music help progress the plot in the scenes without dialogue, for instance, conveying the emotions of Caesar's relationships with Will and Charles. To turn the score into a "driving force that keeps audiences paying attention," Doyle employed an African-American chorus and focused on percussion and "low and deep" orchestra sounds. Doyle collaborated closely with the sound department to make the music complement the sound effects, including writing a recurring theme based on their recording of a chimpanzee.
Answer: The score was written by Patrick Doyle.
Question: Why do gas prices go high in winter
Answer: Prices in a market economy are driven by Supply and demand. Gas prices typically increase in a market economy in a winter because of increase in demand. Demand increases because people use gas to warm homes and cold weather further increases demand for gas. All this increase in demand means gas prices increase. It is not required that gas prices always increase.
Question: Given the following paragraph on gene regulation, what are operons and where do they occur?
Evidence: The regulation of gene expression by environmental factors and during different stages of development can occur at each step of the process such as transcription, RNA splicing, translation, and post-translational modification of a protein. Gene expression can be influenced by positive or negative regulation, depending on which of the two types of regulatory proteins called transcription factors bind to the DNA sequence close to or at a promoter. A cluster of genes that share the same promoter is called an operon, found mainly in prokaryotes and some lower eukaryotes (e.g., Caenorhabditis elegans). In positive regulation of gene expression, the activator is the transcription factor that stimulates transcription when it binds to the sequence near or at the promoter. Negative regulation occurs when another transcription factor called a repressor binds to a DNA sequence called an operator, which is part of an operon, to prevent transcription. Repressors can be inhibited by compounds called inducers (e.g., allolactose), thereby allowing transcription to occur. Specific genes that can be activated by inducers are called inducible genes, in contrast to constitutive genes that are almost constantly active. In contrast to both, structural genes encode proteins that are not involved in gene regulation. In addition to regulatory events involving the promoter, gene expression can also be regulated by epigenetic changes to chromatin, which is a complex of DNA and protein found in eukaryotic cells.
Answer: Operons are clusters of genes that share the same promoter. They occur mainly in prokaryotes and some lower eukaryotes such as Caenorhabditis elegans.
Question: What two colors are used in the Canadian flag?
Evidence: The National Flag of Canada (French: le Drapeau national du Canada), often simply referred to as the Canadian flag or, unofficially, as the maple leaf flag or l'Unifolié (French: [l‿ynifɔlje]; lit. 'the one-leafed'), consists of a red field with a white square at its centre in the ratio of 1∶2∶1, in which is featured a stylized, red, 11-pointed maple leaf charged in the centre. It is the first flag to have been adopted by both houses of Parliament and officially proclaimed by the Canadian monarch as the country's official national flag. The flag has become the predominant and most recognizable national symbol of Canada.
Answer: The Canadian flag colors are red and white.
Question: Who did the USA beat in Women's World Cup final in 2019?
Answer: The Netherlands
Question: Why isn't multi-threading making my Python code faster? How can I fix it.
Answer: Python restricts access to the interpreter with a Global Interpreter Lock, referred to as the GIL, for internal thread-safety requirements. Unfortunately, this design decision restricts only allows one thread to interpret Python code at a time, even on a machine with the capability to run multiple threads together. 

If you wish to run Python code on a set of data, and the data can be broken up into independent segments (a process known as data partitioning), you may wish to look into the multiprocessing library in Python. 

As an example, consider the function foo which takes one argument, arg1, and say you wanted to run the foo function on a number of values, say, val1, val2, and val3.

def foo(arg1):
  # do something

if __name__ == '__main__':
  foo(val1)
  foo(val2)
  foo(val3)

To have foo execute on multiple processes together, you could use the multiprocessing Pool  to run multiple worker processes together:

from multiprocessing import Pool

def foo(arg1):
  # do something

if __name__ == '__main__':
  p = Pool()
  p.map(foo, [val1, val2, val3])

The multiprocessing library also provides various other APIs for parallel programming. Would you like to know more?
Question: How do I get rid of mosquitos in my house?
Answer: You can get rid of mosquitos in your house by keeping doors closed first and foremost.  If mosquitos are already inside your house you can get a mosquito zapper from a local hardware store and put it in the part of your house that has the most mosquitos.  Some areas to consider for mosquitos would be the parts of the house where people spend the most time like the kitchen of where they sleep.  Using the bug zapper during the day is not recommended because mosquitos will find it hard to see the light.  Using it during the night makes the most sense because they will be more attracted to the light.
Question: What is the research background of this paper?
Evidence: Blogging gained momentum in 1999 and became especially popular after the launch of freely available, hosted platforms such as blogger.com or livejournal.com. Blogging has progressively been used by individuals to share news, ideas, and information, but it has also developed a mainstream role to the extent that it is being used by political consultants and news services as a tool for outreach and opinion forming as well as by businesses as a marketing tool to promote products and services BIBREF0 .
For this paper, we compiled a very large geolocated collection of blogs, written by individuals located in the U.S., with the purpose of creating insightful mappings of the blogging community. In particular, during May-July 2015, we gathered the profile information for all the users that have self-reported their location in the U.S., along with a number of posts for all their associated blogs. We utilize this blog collection to generate maps of the U.S. that reflect user demographics, language use, and distributions of psycholinguistic and semantic word classes. We believe that these maps can provide valuable insights and partial verification of previous claims in support of research in linguistic geography BIBREF1 , regional personality BIBREF2 , and language analysis BIBREF3 , BIBREF4 , as well as psychology and its relation to human geography BIBREF5 .
Blogging gained momentum in 1999 and became especially popular after the launch of freely available, hosted platforms such as blogger.com or livejournal.com. Blogging has progressively been used by individuals to share news, ideas, and information, but it has also developed a mainstream role to the extent that it is being used by political consultants and news services as a tool for outreach and opinion forming as well as by businesses as a marketing tool to promote products and services (Nardi et al., 2004).
Answer: Blogging gained momentum in 1999 and became especially popular after the launch of freely available, hosted platforms such as blogger.com or livejournal.com. Blogging has progressively been used by individuals to share news, ideas, and information, but it has also developed a mainstream role to the extent that it is being used by political consultants and news services as a tool for outreach and opinion forming as well as by businesses as a marketing tool to promote products and services.
Question: What is dry ice?
Answer: Dry Ice is frozen carbon dioxide that turns into a gas when defrosts.
Question: Give me a list of the most agreed upon supplements that are effective for maintaining general health
Answer: 1) Vitamin D
2) Omega-3 Fish Oil
3) Creatine Monohydrate
4) Multi-vitamin

*Disclaimer - there are countless other supplements available; however, decades of research seems to have formed a consensus on the efficacy of these
Question: List the British prime ministers since world war 2
Answer: Winston Churchill (1940-1945), Clement Attlee (1945-1951), Winston Churchill (1951-1955), Anthony Eden (1955-1957), Harold Macmillan (1957-1963), Alec Douglas-Home (1963-1964), Harold Wilson (1964-1970), Edward Heath (1970-1974), Harold Wilson (1974-1976), James Callaghan (1976-1979), Margaret Thatcher (1979-1990), John Major (1990-1997), Tony Blair (1997-2007), Gordon Brown (2007-2010), David Cameron (2010-2016), Theresa May (2016-2019), Boris Johnson (2019-2022), Liz Truss (2022), Rishi Sunak (2022-present)
Question: How many data categorization impact levels does the Department of Defense define and provide authorizations for?
Answer: The DoD offers authorizations for IL-2, IL-4, IL-5, and IL-6.
Question: Identify which instrument is string or percussion: Tambourine, Esraj
Answer: Esraj is string, Tambourine is percussion.
Question: What algorithm do they use in the method?
Evidence: In our approach we develop the idea of BIBREF16 that proposed to construct similarity sets between ngram phrases between each other and single words. Phrases and words are included in the same similarity set if they have the same component word, for example, weapon – nuclear weapon – weapon of mass destruction; discrimination – racial discrimination. It was supposed that if expressions from the same similarity set co-occur in the same document then their contribution into the document's topics is really more than it is presented with their frequencies, therefore their frequencies should be increased. In such an approach, the algorithm can "see" similarities between different multiword expressions with the same component word.
In our approach, at first, we include related single words and phrases from a thesaurus such as WordNet or EuroVoc in these similarity sets. Then, we add preliminarily extracted ngrams into these sets and, this way, we use two different sources of external knowledge. We use the same LDA-SIM algorithm as described in BIBREF16 but study what types of semantic relations can be introduced into such similarity sets and be useful for improving topic models. The pseudocode of LDA-SIM algorithm is presented in Algorithm SECREF3 , where INLINEFORM0 is a similarity set, expressions in similarity sets can comprise single words, thesaurus phrases or generated noun compounds.
We can compare this approach with the approaches applying the generalized Polya urn model BIBREF8 , BIBREF9 , BIBREF10 . To add prior knowledge, those approaches change topic distributions for related words globally in the collection. We modify topic probabilities for related words and phrases locally, in specific texts, only when related words (phrases) co-occur in these texts.
[ht!] collection INLINEFORM0 , vocabulary INLINEFORM1 , number of topics INLINEFORM2 , initial INLINEFORM3 and INLINEFORM4 , sets of similar expressions INLINEFORM5 , hyperparameters INLINEFORM6 and INLINEFORM7 , INLINEFORM8 is the frequency of INLINEFORM9 in the document INLINEFORM10 distributions INLINEFORM11 and INLINEFORM12 not meet the stop criterion INLINEFORM13 INLINEFORM14 
 INLINEFORM0 INLINEFORM1 
 INLINEFORM0 
 INLINEFORM0 
 LDA-SIM algorithm
We use the same LDA-SIM algorithm as described in [17] but study what types of semantic relations can be introduced into such similarity sets and be useful for improving topic models.
Answer: LDA-SIM algorithm.
Question: What is the largest American state by land mass?
Answer: Alaska is the largest state in the United States
Question: What is Mozart’s contribution to the music?
Evidence: Wolfgang Amadeus Mozart (27 January 1756 – 5 December 1791) was a prolific and influential composer of the Classical period. Despite his short life, his rapid pace of composition resulted in more than 800 works of virtually every genre of his time. Many of these compositions are acknowledged as pinnacles of the symphonic, concertante, chamber, operatic, and choral repertoire. Mozart is widely regarded as among the greatest composers in the history of Western music, with his music admired for its "melodic beauty, its formal elegance and its richness of harmony and texture".
Answer: Mozart contributed more than 800 pieces of music that are known for melodic beauty, elegance and richness.
Question: What models that rely only on claim-specific linguistic features are used as baselines?
Evidence: Similar to BIBREF9, we experiment with SVM with RBF kernel, with features that represent (1) the simple characteristics of the argument tree and (2) the linguistic characteristics of the claim.
Similar to BIBREF9, we experiment with SVM with RBF kernel, with features that represent (1) the simple characteristics of the argument tree and (2) the linguistic characteristics of the claim.
Answer: The answers are shown as follows:
* SVM with RBF kernel
Question: Tell me whether these cities are in Italy or Switzerland: Milan, Florence, Zurich, Bern, Lucerne, Rome
Answer: Milan: Italy
Florence: Italy
Zurich: Switzerland
Bern: Switzerland
Lucerne: Switzerland
Rome: Italy
Question: Please summarize from this text what were the main reasons Czech people immigrated to Texas.
Evidence: Czech immigration was driven by the availability of land in Texas and by events in Europe. In the 1840s, the Austrian Empire, which included Czech lands, was still run by a feudal system, despite the wave of democratic ideals permeating western Europe. This led to the Revolutions of 1848, which did little to improve the economic status of peasants who still found it difficult to gain wealth. The revolutions, together with factors such as religious persecution and mandatory military service, fueled a wave of emigration from Austria beginning in 1852. Immigrants to America were unfamiliar with the concept of American democracy due to their lack of experience with democratic ideals. The concepts of states' rights and slavery were equally foreign to the immigrants.
Answer: Czech immigration to Texas in the 19th century was influenced by the availability of land in Texas to homestead, economic opportunity, freedom from religious persecution and to escape the revolutions in the Austrian empire that lead to political unrest and economic uncertainty in the region.
Question: Tell me which of the following are real car parts: differential, gearbox, splinehook, flidget, wheel.
Answer: Differential, gearbox, and wheels are all parts of cars.
Question: How to realize and validate the experiment by the authors?
Evidence: In this section, we describe the experiments and report the results for the classification of Wikipedia medical articles into the six classes of the Wikipedia Medicine Portal. We compare the results obtained adopting four different classifiers: the actionable model in BIBREF7 and three classifiers that leverage the ad-hoc features from the medical domain discussed in the previous sections. All the experiments were realized within the Weka framework BIBREF17 and validated through 10 fold cross-validation.
For each experiment, we relied on the dataset presented in Section "Dataset" , and specifically, on that obtained after sampling the majority classes and oversampling the minority ones (right-hand column in Table 1 ). The dataset serves both as training and test set for the classifiers.
Moreover, to take into account the imbalanced data, we have applied several classification algorithms and, for the sake of conciseness, hereafter we report only the best results we have achieved. In particular, we have experimented with bagging, adaptive boosting and random forest and we report the results for the latter only.
All the experiments were realized within the Weka framework [14] and validated through 10 fold cross-validation.
Answer: All the experiments were realized within the Weka framework and validated through 10 fold cross-validation.
Question: Tell me whether these cities are in the United States or not: Dallas, Seattle, Mumbai, Ljubljana, Rochester
Answer: Dallas, Seattle, and Rochester are all cities in the United States. Mumbai and Ljubljana are not in the United States.
Question: What is a 'Public' School in the UK?
Answer: Public School is a term used for an independent fee-paying school in the UK.  This is because when the schools were initially set-up, they were for the use of fee-paying parents who would share a teacher as a replacement for a dedicated at-home tutor for their children
Question: How does ZRTools work?
Evidence: For UTD we use the Zero Resource Toolkit (ZRTools; Jansen and Van Durme, 2011). ZRTools uses dynamic time warping (DTW) to discover pairs of acoustically similar audio segments, and then uses graph clustering on overlapping pairs to form a hard clustering of the discovered segments. Replacing each discovered segment with its unique cluster label, or pseudoterm, gives us a partial, noisy transcription, or pseudotext (Fig. FIGREF4 ).
In creating a translation model from this data, we face a difficulty that does not arise in the parallel texts that are normally used to train translation models: the pseudotext does not represent all of the source words, since the discovered segments do not cover the full audio (Fig. FIGREF4 ). Hence we must not assume that our MT model can completely recover the translation of a test sentence. In these conditions, the language modeling and ordering assumptions of most MT models are unwarranted, so we instead use a simple bag-of-words translation model based only on co-occurrence: IBM Model 1 BIBREF11 with a Dirichlet prior over translation distributions, as learned by fast_align BIBREF12 . In particular, for each pseudoterm, we learn a translation distribution over possible target words. To translate a pseudoterm in test data, we simply return its highest-probability translation (or translations, as discussed in § SECREF5 ).
This setup implies that in order to translate, we must apply UTD on both the training and test audio. Using additional (not only training) audio in UTD increases the likelihood of discovering more clusters. We therefore generate pseudotext for the combined audio, train the MT model on the pseudotext of the training audio, and apply it to the pseudotext of the test data. This is fair since the UTD has access to only the audio.
ZRTools uses dynamic time warping (DTW) to discover pairs of acoustically similar audio segments, and then uses graph clustering on overlapping pairs to form a hard clustering of the discovered segments. Replacing each discovered segment with its unique cluster label, or pseudoterm, gives us a partial, noisy transcription, or pseudotext (Fig. 1).
Answer: ZRTools uses dynamic time warping (DTW) to discover pairs of acoustically similar audio segments, and then uses graph clustering on overlapping pairs to form a hard clustering of the discovered segments. Replacing each discovered segment with its unique cluster label, or pseudoterm, gives them a partial, noisy transcription, or pseudotext.
Question: Who is the company ABB
Evidence: ABB Ltd. is a Swedish-Swiss multinational corporation headquartered in Zürich, Switzerland. The company was formed in 1988 when Sweden's Allmänna Svenska Elektriska Aktiebolaget (ASEA) and Switzerland's Brown, Boveri & Cie merged to create ASEA Brown Boveri, later simplified to the initials ABB. Both companies were established in the late 1800s and were major electrical equipment manufacturers, a business that ABB remains active in today. The company has also since expanded to robotics and automation technology.
Answer: ABB Ltd. is a Swedish-Swiss multinational corporation headquartered in Zürich, Switzerland. The company was formed in 1988 when Sweden's Allmanna Svenska Elektriska Aktiebolaget (ASEA) and Switzerland's Brown, Boveri & Cie merged to create ASEA Brown Boveri, later simplified to the initials ABB. 

Both companies were established in the late 1800s and were major electrical equipment manufacturers, a business that ABB remains active in today. The company has also since expanded to robotics and automation technology.
Question: What is Spatial Analysis?
Answer: Spatial analysis is any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques using different analytic approaches, especially spatial statistics. In may be applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, or to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is geospatial analysis, the technique applied to structures at the human scale, most notably in the analysis of geographic data. It may also be applied to genomics, as in transcriptomics data.

Complex issues arise in spatial analysis, many of which are neither clearly defined nor completely resolved, but form the basis for current research. The most fundamental of these is the problem of defining the spatial location of the entities being studied. Classification of the techniques of spatial analysis is difficult because of the large number of different fields of research involved, the different fundamental approaches which can be chosen, and the many forms the data can take.