DataHammer/scimrc huggingface dataset:
Question: What score was used to report the ROUGE F1 scores for both datasets of all the competing models?
Evidence: We report the ROUGE F1 scores for both datasets of all the competing models using ROUGE F1 scores BIBREF27 . We report the results on the Gigaword and the CNN dataset in Table 2 and Table 3 , respectively. In Gigaword dataset where the texts are short, our best model achieves a comparable performance with the current state-of-the-art. In CNN dataset where the texts are longer, our best model outperforms all the previous models. We emphasize that E2T module is easily attachable to better models, and we expect E2T to improve their performance as well. Overall, E2T achieves a significant improvement over the baseline model base, with at least 2 ROUGE-1 points increase in the Gigaword dataset and 6 ROUGE-1 points increase in the CNN dataset. In fact, all variants of E2T gain improvements over the baseline, implying that leveraging on linked entities improves the performance of the summarizer. Among the model variants, the CNN-based encoder with selective disambiguation and firm attention performs the best.
Automatic evaluation on the Gigaword dataset shows that the CNN and RNN variants of base+E2T have similar performance. To break the tie between both models, we also conduct human evaluation on the Gigaword dataset. We instruct two annotators to read the input sentence and rank the competing summaries from first to last according to their relevance and fluency: (a) the original summary gold, and from models (b) base, (c) base+E2Tcnn, and (d) base+E2Trnn. We then compute (i) the proportion of every ranking of each model and (ii) the mean rank of each model. The results are reported in Table 4 . The model with the best mean rank is base+E2Tcnn, followed by gold, then by base+E2Trnn and base, respectively. We also perform ANOVA and post-hoc Tukey tests to show that the CNN variant is significantly ( $p<0.01$ ) better than the RNN variant and the base model. The RNN variant does not perform as well as the CNN variant, contrary to the automatic ROUGE evaluation above. Interestingly, the CNN variant produces better (but with no significant difference) summaries than the gold summaries. We posit that this is due to the fact that the article title does not correspond to the summary of the first sentence.
We report the ROUGE F1 scores for both datasets of all the competing models using ROUGE F1 scores (Lin, 2004).
Answer: ROUGE F1 scores (Lin, 2004).
Question: Given these paragraphs about battery storage power stations, how much was able to store and dispatch the first phase of Vistra Energy's Moss Landing Energy Storage Facility?
Evidence: As of 2021, the power and capacity of the largest individual battery storage power plants is an order of magnitude less than that of the largest pumped storage power plants, the most common form of grid energy storage. For example, the Bath County Pumped Storage Station, the second largest in the world, can store 24GWh of electricity and dispatch 3GW while the first phase of Vistra Energy's Moss Landing Energy Storage Facility can store 1.2GWh and dispatch 300MW. Grid batteries do not however have to be large, and smaller ones can be deployed widely across a grid for greater redundancy.

As of 2019, battery power storage is cheaper than open cycle gas turbine power for use up to two hours, and there was around 365 GWh of battery storage deployed worldwide, growing extremely rapidly. Levelized cost of electricity from battery storage has fallen rapidly, halving in two years to US$150 per MWh as of 2020.
Answer: The first phase of Vistra Energy's Moss Landing Energy Storage Facility can store 1.2GWh and dispatch 300MW.
Question: Based on this wikipedia paragraph, what original tool was Kubernetes based on?
Evidence: Kubernetes (κυβερνήτης kubernḗtēs, Greek for "steersman, navigator" or "guide", and the etymological root of cybernetics) was announced by Google in mid-2014. The project was created by Joe Beda, Brendan Burns, and Craig McLuckie, who were soon joined by other Google engineers, including Brian Grant and Tim Hockin.

The design and development of Kubernetes was influenced by Google's Borg cluster manager. Many of its top contributors had previously worked on Borg; they codenamed Kubernetes "Project 7" after the Star Trek ex-Borg character Seven of Nine and gave its logo a seven-spoked wheel. Unlike Borg, which was written in C++, Kubernetes source code is in the Go language.

Kubernetes 1.0 was released on July 21, 2015. Google worked with the Linux Foundation to form the Cloud Native Computing Foundation (CNCF) and offered Kubernetes as a seed technology. In February 2016, the Helm package manager for Kubernetes was released.
Answer: Kubernetes was originally based on Google's Borg cluster manager tool.
Question: Who invented the telephone?
Answer: In 1876, Alexander Graham Bell was the first to obtain a United States patent for a device that produced a clearly intelligible replica of the human voice on a second device.
A resolution passed by the United States House of Representatives on June 11, 2002 called for recognition of Antonio Meucci's work and contributions towards the invention of the telephone.
Question: What's the differences of the Wing Chun kongfu and the traditional Shaolin Kongfu?
Answer: Wing chun was invented by a Buddhist nun who watched a snake and crane fighting. She adopted principles from both styles. She equated that to yin vs yang in terms of fighting style. 
It's all about economy of movement, small quick strikes and close quarters combat. 
It's meant to be effective for people of all ages and sizes and does not emphasize force. The crane stood there motionless and waded off the snakes quick strikes. It wasn't phased by the attacks. The posture and mindset of the crane and the quick strikes of the snake. 

Shaolin was developed to ward off invaders. Usually enemies riding horses so they do a lot of high jumping kicks which were used to kick people off horses. 
It's usually associated with tiger or dragon styles which can operate at more distance and use power. It's also very flashy and usedin demonstrations and many king fu movies .
It's actually counterintuitive to many martial arts where you strike hard or from a distance and move around a lot ducking blocking etc. Wing chun is just small subtle blocks to divert attacks off center and then you strike back concurrently
Question: Provide a bulleted list of the different shapes of lenses
Evidence: Construction of Simple Lenses

Most lenses are spherical lenses: their two surfaces are parts of the surfaces of spheres. Each surface can be convex (bulging outwards from the lens), concave (depressed into the lens), or planar (flat). The line joining the centres of the spheres making up the lens surfaces is called the axis of the lens. Typically the lens axis passes through the physical centre of the lens, because of the way they are manufactured. Lenses may be cut or ground after manufacturing to give them a different shape or size. The lens axis may then not pass through the physical centre of the lens.

Toric or sphero-cylindrical lenses have surfaces with two different radii of curvature in two orthogonal planes. They have a different focal power in different meridians. This forms an astigmatic lens. An example is eyeglass lenses that are used to correct astigmatism in someone's eye.
Answer: The following are the different shapes of lenses:
- Convex: Curves out from the middle of the lens
- Concave: Curves into the middle of the lens
- Planar: Flat lens
Question: Considering Austin is in the central region of Texas, which of these places are in east, west, north and south. Houston, Dallas, San Antonio, Fredericksburg, El Paso, Corpas Christi, Galveston, South Padre, Lubbock.
Answer: North of Austin: Dallas
South of Austin: San Antonio, Corpus Christi, South Padre
East of Austin: Houston, Galveston
West of Austin: Fredericksburg, Lubbock, El Paso
Question: Is there a high school in Fairdale
Evidence: Fairdale High School is a high school located in the Fairdale area of Louisville, Kentucky, United States. It is part of the Jefferson County Public Schools. As of the 2013–14 school year, there were 1,804 students at the school.
Answer: Yes. The Fairdale High School is a high school located in the Fairdale area of Louisville, Kentucky, United States. It is part of the Jefferson County Public Schools. As of the 2013–14 school year, there were 1,804 students at the school.
Question: What is the oldest known civilization?
Evidence: Scholars generally acknowledge six cradles of civilization. Mesopotamia, Ancient Egypt, Indus Valley, and Ancient China are believed to be the earliest in the Old World. Cradles of civilization in the New World are the Caral-Supe civilization of coastal Peru and the Olmec civilization of Mexico. All of the cradles of civilization depended upon agriculture for sustenance (except possibly Caral-Supe which may have depended initially on marine resources). All depended upon farmers producing an agricultural surplus to support the centralized government, political leaders, priests, and public works of the urban centers of the civilization.

Less formally, the term "cradle of civilization" is often used to refer to other historic ancient civilizations, such as Greece or Rome, which have both been called the "cradle of Western civilization".
Answer: The oldest known human civilizations are known to exist in Mesopotamia (Modern day Iraq), Indus Valley (a region located in modern-day Pakistan), Ancient Egypt and Ancient China.
Question: How much better were results of the proposed models than base LSTM-RNN model?
Evidence: FLOAT SELECTED: Table 1: Automatic Evaluation Activity/Entity F1 results for baselines and our 3 models (attn means “with attention”). LSTM, HRED and VHRED are reported in Serban et al. (2017a), VHRED (attn) and Reranking-RL in Niu and Bansal (2018a), and the rest are produced by our work. All our four models have statistically significantly higher F1 values (p < 0.001) against VHRED (attn) and MMI.
FLOAT SELECTED: Table 1: Automatic Evaluation Activity/Entity F1 results for baselines and our 3 models (attn means “with attention”). LSTM, HRED and VHRED are reported in Serban et al. (2017a), VHRED (attn) and Reranking-RL in Niu and Bansal (2018a), and the rest are produced by our work. All our four models have statistically significantly higher F1 values (p < 0.001) against VHRED (attn) and MMI.
Answer: on diversity 6.87 and on relevance 4.6 points higher
Question: Categorize the following as do's and don'ts when opening a new bank account:
research bank options
find the cheapest option
know what kinds of accounts they offer
know interest rates
understand discounts
open as many accounts as you can
talk to an advisor
go by hearsay
Answer: Do's:
research bank options
know what kinds of accounts they offer
know interest rates
understand discounts
talk to an advisor

Dont's:
find the cheapest option
open as many accounts as you can
go by hearsay
Question: How many people visit the zoo in the US each year?
Evidence: A zoo (short for zoological garden; also called an animal park or menagerie) is a facility in which animals are kept within enclosures for public exhibition and often bred for conservation purposes.

The term zoological garden refers to zoology, the study of animals. The term is derived from the Greek ζώον, zoon, 'animal', and the suffix -λογία, -logia, 'study of'. The abbreviation zoo was first used of the London Zoological Gardens, which was opened for scientific study in 1828 and to the public in 1847. In the United States alone, zoos are visited by over 181 million people annually.
Answer: 181 million people in the US visit the zoo annually each year.
Question: Given this paragraph about Africa, why Africa's per capita  GDP is low?
Evidence: Africa is the world's second-largest and second-most populous continent, after Asia in both aspects. At about 30.3 million km2 (11.7 million square miles) including adjacent islands, it covers 20% of Earth's land area and 6% of its total surface area. With 1.4 billion people as of 2021, it accounts for about 18% of the world's human population. Africa's population is the youngest amongst all the continents; the median age in 2012 was 19.7, when the worldwide median age was 30.4. Despite a wide range of natural resources, Africa is the least wealthy continent per capita and second-least wealthy by total wealth, behind Oceania. Scholars have attributed this to different factors including geography, climate, tribalism, colonialism, the Cold War, neocolonialism, lack of democracy, and corruption. Despite this low concentration of wealth, recent economic expansion and the large and young population make Africa an important economic market in the broader global context.
Answer: While there are several reasons high population growth, corruption and colonialism and neocolonialism are some of the the factors.
Question: Which movies star Eddie Murphy and which ones star Bill Murray: Groundhog Day, Coming to America, Rushmore, Lost in Translation, Beverly Hills Cop, Caddyshack, 48 Hours, and Trading Places.
Answer: Eddie Murphy was in Coming to America, Beverly Hills Cop, 48 Hours, and Trading Places. Bill Murray was in Groundhog Day, Rushmore, Lost in Translation, and Caddyshack.
Question: from this blob of text about gosberton clough, to which city does the river flow to?
Evidence: Gosberton Clough is a village in the civil parish of Gosberton and the South Holland district of Lincolnshire, England. It is 30 miles (50 km) south-east from the city and county town of Lincoln, 5 miles (8 km) north from the nearest large town of Spalding, and 3 miles (5 km) west-southwest from parish village of Gosberton.

Gosberton Clough and the village Risegate to the east are conjoined as a 2 miles (3 km) linear settlement on the east to west B1397 road which runs along the south side of Risegate Eau (drain). Within Gosberton Clough the B1397 is named 'Clough Road', and in Risegate, 'Risegate Road', the villages separated at a bridge over the Risegate Eau at the junction with Chesboule Lane, running north, and Beach Lane, running south. The B1397 and the village is mirrored at the north of Risegate Eau by the parallel 'Siltside' (road). The Risegate Eau starts 2 miles (3 km) west at the South Forty-Foot Drain, then flows through the village, and reaches the River Welland at the Risegate Outfall sluice in Algarkirk Marsh, 7 miles (11 km) to the east. The north to south Hammond Beck intersects Risegate Eau at the west of the village, alongside of which is Beck Bank (road).
Answer: Towards Lincoln, England
Question: Who are the Georgia Bulldogs' biggest rival?
Answer: Georgia has many traditional geographical rivals.  The answer typically depends on who you ask and generally aligns with the age of the Georgia fan.  Older fans tend to oppose the Georgia Tech Yellowjackets or Auburn the most as they fondly remember competitive annual matchups in the rivalry games dubbed Clean Old Fashioned Hate or The Deep Souths Oldest Rivalry.  GenX and Millennials typically point to the Florida Gators since Florida went on a two decade long run of dominance, going 18-3 in between 1990 and 2010.  Tennessee might also garner some votes in this timeframe.  Younger fans might point to the multiple National Championship crushing heartbreaks at the hands of Alabama in 2008, 2012, 2015, 2017, and 2018.
Question: What are three effective approaches to improve weight loss?
Answer: 1.  Maximizing your potential through hormone replacement therapy will improve your ability to recover faster, improve your sleep, and improve your workout intensity. 
2.  Along with a consistent and ridged workout regimen of 4-5x per week, it's important get plenty of sleep and drink 1-2 liters of water per day.
3.  Maximizing your diet through grass fed animal proteins and other whole foods while avoiding processed foods that contain seed oils, natural flavors, added sugar, and other additives is a great way to start your weight loss journey.
Question: How was Naive Bayes model learned?
Evidence: We employ three estimation schemes based on the textual contexts of concepts. The first relies on the context via pre-trained word embeddings of the concepts, which, in turn, are derived from the concepts' distributional properties in large samples of free texts. The other two schemes directly access the sentence-level contexts of the concepts.
Nearest neighbors (NN) Estimator: We used the pre-trained GloVe embeddings BIBREF11 of concepts to implement a nearest-neighbor estimator as follows. Given a concept $c$, we extract all labeled concepts within a given radius $r$ (cosine similarity $0.3$). In one variant, $c$'s controversiality score is taken to be the fraction of controversial concepts among them. In another variant, labeled concepts are weighted by their cosine similarity to $c$.
Naive Bayes (NB) Estimator: A Naive Bayes model was learned, with a bag-of-words feature set, using the word counts in the sentences of our training data – the contexts of the controversial and non-controversial concepts. The controversiality score of a concept $c$ for its occurrence in a sentence $s$, is taken as the posterior probability (according to the NB model) of $s$ to contain a controversial concept, given the words of $s$ excluding $c$, and taking a prior of $0.5$ for controversiality (as is the case in the datasets). The controversiality score of $c$ is then defined as the average score over all sentences referencing $c$.
Recurrent neural network (RNN): A bidirectional RNN using the architecture suggested in BIBREF10 was similarly trained. The network receives as input a concept and a referring sentence, and outputs a score. The controversiality score of a concept is defined, as above, to be the average of these scores.
Naive Bayes (NB) Estimator: A Naive Bayes model was learned, with a bag-of-words feature set, using the word counts in the sentences of our training data – the contexts of the controversial and non-controversial concepts. The controversiality score of a concept c for its occurrence in a sentence s, is taken as the posterior probability (according to the NB model) of s to contain a controversial concept, given the words of s excluding c, and taking a prior of 0.5 for controversiality (as is the case in the datasets). The controversiality score of c is then defined as the average score over all sentences referencing c.
Answer: The controversiality score of a concept c for its occurrence in a sentence s, is taken as the posterior probability (according to the NB model) of s to contain a controversial concept, given the words of s excluding c, and taking a prior of 0.5 for controversiality (as is the case in the datasets). The controversiality score of c is then defined as the average score over all sentences referencing c.
Question: What is the best kind of dog breed?
Answer: The best dog breed is a golden retriever. They are loyal, loving, smart dogs who love all things in the world. In addition, they are the cutest dogs you may ever see and their eyes look into your soul. Their biggest flaw is that they shed, but you just need to wear less black clothing and it won't matter as much. Everyone has a unique opinion about what the best dog breed is.
Question: In theorem 1, did not see a derivation for the last term O(sqrt(ln(1/\delta)/n) in the proof. How is that derived?
Evidence: We use a slightly modified version of the standard empirical Rademacher complexity bound on generalisation error, as stated by Mohri et al. (2018) but originally shown by Bartlett & Mendelson (2002), where the modification is to weaken the i.i.d. assumption to be only an independence assumption—i.e., allow instances to be drawn from different distributions. The proof is exactly the same (because McDiarmid’s inequality requires only independence, and not identical distributions), but we re-state it here for completeness.
Answer: These arise due to McDiarmid’s inequality. We have added more detail to the proof to clarify this.
Question: What hindi song was used during the credits for the 2006 movie, 'Inside Man', starring Denzel Washington ?
Answer: The song that was used during the credits was 'Chalo Chaiyya Chaiyaa'  - it was originally used in the Bollywood movie Dil Se and Spike Lee liked it so much he used it in his movie.
Question: Do they use pretrained word embeddings?
Evidence: Trafficking detection: There have been several software products designed to aid anti-trafficking efforts. Examples include Memex which focuses on search functionalities in the dark web; Spotlight which flags suspicious ads and links images appearing in multiple ads; Traffic Jam which seeks to identify patterns that connect multiple ads to the same trafficking organization; and TraffickCam which aims to construct a crowd-sourced database of hotel room images to geo-locate victims. These research efforts have largely been isolated, and few research articles on machine learning for trafficking detection have been published. Closest to our work is the Human Trafficking Deep Network (HTDN) BIBREF9 . HTDN has three main components: a language network that uses pretrained word embeddings and a long short-term memory network (LSTM) to process text input; a vision network that uses a convolutional network to process image input; and another convolutional network to combine the output of the previous two networks and produce a binary classification. Compared to the language network in HTDN, our model replaces LSTM with a gated-feedback recurrent neural network, adopts certain regularizations, and uses an ordinal regression layer on top. It significantly improves HTDN's benchmark despite only using text input. As in the work of E. Tong et al. ( BIBREF9 ), we pre-train word embeddings using a skip-gram model BIBREF4 applied to unlabeled data from escort ads, however, we go further by analyzing the emojis' embeddings and thereby expand the trafficking lexicon.
As in the work of E. Tong et al. ( BIBREF9 ), we pre-train word embeddings using a skip-gram model BIBREF4 applied to unlabeled data from escort ads, however, we go further by analyzing the emojis' embeddings and thereby expand the trafficking lexicon.
Answer: Yes.
Question: Write me a recursive function for calculating the nth fibonacci number
Answer: Below is a recursive Python function fib(n) that computes the nth Fibonacci number. This is split into a base case and a recursive case. The base case is given by the fact that the first two terms of the Fibonacci sequence are 0 and 1, so fib(0)=0 and fib(1)=1. The recursive case is given by the fact that the nth Fibonacci number f_n can be written as the sum of the previous two Fibonacci numbers, so f_n = f_{n-1} + f_{n-2}. Putting this all together, we get

```
def fib(n):
	# base case
	if n == 0:
		return 0
	if n == 1:
		return 1

	# recursive case
	return fib(n - 1) + fib(n - 2)
```

This will correctly calculate the nth Fibonacci number for nonnegative integer inputs n. However, recursively calculating Fibonacci numbers in this way takes exponential time in the value of n, so this code may be very slow for large inputs. It could be improved by memoizing previous results, or using a non-recursive approach. Simple iterative approaches can solve the problem more quickly, and closed-form expressions for the nth Fibonacci number exist and are even faster to evaluate.