DataHammer/scimrc huggingface dataset:
Question: What is the Pizza history?
Evidence: Modern pizza evolved from similar flatbread dishes in Naples, Italy, in the 18th or early 19th century.
The word pizza was first documented in AD 997 in Gaeta and successively in different parts of Central and Southern Italy. Pizza was mainly eaten in Italy and by emigrants from there. This changed after World War II when Allied troops stationed in Italy came to enjoy pizza along with other Italian foods.
Answer: The history of pizza begins in antiquity, as various ancient cultures produced basic flatbreads with several toppings.

A precursor of pizza was probably the focaccia, a flatbread known to the Romans as panis focacius, to which toppings were then added. Modern pizza evolved from similar flatbread dishes in Naples, Italy, in the 18th or early 19th century.

The word pizza was first documented in AD 997 in Gaeta and successively in different parts of Central and Southern Italy. Pizza was mainly eaten in Italy and by emigrants from there. This changed after World War II when Allied troops stationed in Italy came to enjoy pizza along with other Italian foods.
Some commentators have suggested that the origins of modern pizza can be traced to pizzarelle, which were kosher for Passover cookies eaten by Roman Jews after returning from the synagogue on that holiday, though some also trace its origins to other Italian paschal bread. Other examples of flatbreads that survive to this day from the ancient Mediterranean world are focaccia (which may date back as far as the ancient Etruscans); Manakish in the Levant, coca (which has sweet and savory varieties) from Catalonia, Valencia and the Balearic Islands; the Greek Pita; Lepinja in the Balkans; or Piadina in the Romagna part of Emilia-Romagna in Italy
Question: From the passage provided, list the major APIs in Kafka. Separate them with a comma.
Evidence: Kafka stores key-value messages that come from arbitrarily many processes called producers. The data can be partitioned into different "partitions" within different "topics". Within a partition, messages are strictly ordered by their offsets (the position of a message within a partition), and indexed and stored together with a timestamp. Other processes called "consumers" can read messages from partitions. For stream processing, Kafka offers the Streams API that allows writing Java applications that consume data from Kafka and write results back to Kafka. Apache Kafka also works with external stream processing systems such as Apache Apex, Apache Beam, Apache Flink, Apache Spark, Apache Storm, and Apache NiFi.

Kafka runs on a cluster of one or more servers (called brokers), and the partitions of all topics are distributed across the cluster nodes. Additionally, partitions are replicated to multiple brokers. This architecture allows Kafka to deliver massive streams of messages in a fault-tolerant fashion and has allowed it to replace some of the conventional messaging systems like Java Message Service (JMS), Advanced Message Queuing Protocol (AMQP), etc. Since the 0.11.0.0 release, Kafka offers transactional writes, which provide exactly-once stream processing using the Streams API.

Kafka supports two types of topics: Regular and compacted. Regular topics can be configured with a retention time or a space bound. If there are records that are older than the specified retention time or if the space bound is exceeded for a partition, Kafka is allowed to delete old data to free storage space. By default, topics are configured with a retention time of 7 days, but it's also possible to store data indefinitely. For compacted topics, records don't expire based on time or space bounds. Instead, Kafka treats later messages as updates to older message with the same key and guarantees never to delete the latest message per key. Users can delete messages entirely by writing a so-called tombstone message with null-value for a specific key.

There are five major APIs in Kafka:

Producer API – Permits an application to publish streams of records.
Consumer API – Permits an application to subscribe to topics and processes streams of records.
Connector API – Executes the reusable producer and consumer APIs that can link the topics to the existing applications.
Streams API – This API converts the input streams to output and produces the result.
Admin API – Used to manage Kafka topics, brokers, and other Kafka objects.
The consumer and producer APIs are decoupled from the core functionality of Kafka through an underlying messaging protocol. This allows writing compatible API layers in any programming language that are as efficient as the Java APIs bundled with Kafka. The Apache Kafka project maintains a list of such third party APIs.
Answer: Producer API, Consumer API, Connector API, Streams API, Admin API
Question: What team did Bobby Orr play for in his 5th season?
Evidence: Robert Gordon Orr OC (born March 20, 1948) is a Canadian former professional ice hockey player, widely acknowledged as one of the greatest of all time. Orr used his ice skating speed, scoring, and play-making abilities to revolutionize the position of defenceman. He played in the National Hockey League (NHL) for 12 seasons, the first 10 with the Boston Bruins, followed by two with the Chicago Black Hawks. Orr remains the only defenceman to have won the league scoring title with two Art Ross Trophies. He holds the record for most points and assists in a single season by a defenceman. Orr won a record eight consecutive Norris Trophies as the NHL's best defenceman and three consecutive Hart Trophies as the league's most valuable player (MVP). Orr was inducted into the Hockey Hall of Fame in 1979 at age 31, the youngest to be inducted at that time. In 2017, Orr was named by the National Hockey League as one of the "100 Greatest NHL Players" in history.
Answer: Bobby Orr played for the Boston Bruins in his 5th season in the National Hockey League (NHL).

He played in the NHL for 12 seasons, the first 10 with the Boston Bruins, followed by two with the Chicago Black Hawks.
Question: write a short essay on California wild fire
Answer: California is known for its hot, dry summers, which increase the risk of wildfires. Every year, the state experiences dozens of fires, which can be devastating to both the environment and people's homes and businesses.

In recent years, California has seen some of the worst wildfires in its history, with tens of thousands of acres of land destroyed and hundreds of homes lost. These fires are often started by a combination of high temperatures, dry conditions, and strong winds, which can quickly spread flames and make them difficult to control.

The effects of these fires are felt not just by those directly impacted, but by everyone in the state. Smoke and ash from the fires can cause respiratory problems and other health issues, and the destruction of habitats can harm wildlife populations and disrupt entire ecosystems.

Despite the challenges, California has made significant progress in fighting wildfires in recent years. The state has invested in new technologies and equipment to help firefighters respond more quickly and effectively to fires, and has increased efforts to prevent fires from starting in the first place.

While wildfires will likely always be a risk in California, the state's proactive approach to managing them gives hope that we can continue to protect people, wildlife, and the environment from their devastating effects.
Question: From the passage provided, extract the awards received by Jensen Huang from 1999-2021.
Evidence: In 1999, Jensen Huang was named Entrepreneur of the Year in High Technology by Ernst & Young.[citation needed] In 2003, Huang received the Dr. Morris Chang Exemplary Leadership Award, which recognizes a leader who has made exceptional contributions to driving the development, innovation, growth, and long-term opportunities of the fabless semiconductor industry, from the Fabless Semiconductor Association. He was also a National Finalist for the EY Entrepreneur of the Year Award in 2003 and was an Award Recipient for the Northern California region in 1999.[citation needed]

Additionally, Huang is a recipient of the Daniel J. Epstein Engineering Management Award from the University of Southern California and was named an Alumni Fellow by Oregon State University. Huang was awarded an honorary doctorate from Oregon State University at the June 13, 2009, commencement ceremony.

In 2018, Huang was listed in the inaugural Edge 50, naming the world's top 50 influencers in edge computing. In October 2019, Harvard Business Review named Jensen Huang best-performing CEO in the world. In November 2020, Jensen Huang was named "Supplier CEO of the year" by Eurostars AutomotiveNewsEurope. Huang was awarded an honorary doctorate from National Taiwan University at the school anniversary in November, 2020.[better source needed] In August 2021, the Semiconductor Industry Association (SIA) announced that Jensen Huang is the 2021 recipient of the industry’s highest honor, the Robert N. Noyce Award. In September 2021, he was included in the Time 100, Time's annual list of the 100 most influential people in the world.
Answer: Jensen Huang's awards include: 1) Entrepreneur of the Year in High Technology by Ernst & Young, 2) Dr. Morris Chang Exemplary Leadership Award, 3) National Finalist for the EY Entrepreneur of the Year Award, 4) Award Recipient for the Northern California region, 5) Daniel J. Epstein Engineering Management Award, 6) Alumni Fellow of Oregon State University, 7) honorary doctorate from Oregon State University, 8) Edge 50 member 9) best-performing CEO in the world 10) Supplier CEO of the year, 11) honorary doctorate from National Taiwan University, 12) Robert N. Noyce Award, 13) Time 100 member.
Question: Classify each of the following as either a classical or jazz artist
Answer: Richard Wagner, Miles Davis, Ella Fitzgerald, Frederic Chopin, Amadeus Mozart, Dave Brubeck, Claude Debussy, Thelonious Monk, Robert Schumann, Louis Armstrong
Question: How did Andy Warhol create the "piss paintings"?
Answer: Andy Warhol invited staffers who worked with him to help him paint by urinating on canvases that were covered by Warhol in copper paint. The urine reacted with the copper paint chemically to create different colours. The urine also created physical impacts on the copper paint on the canvas.
Question: Why is the immigration system broken?
Answer: The immigration system is broken because it is based on outdated policies and procedures, and does not reflect the realities of today's global economy. The current system is too expensive and inefficient, and fails to provide a fair and equitable solution for all migrants.
Question: What class of locomotive was the No. 13?
Evidence: The OA class is a solitary steam locomotive built by the Baldwin Locomotive Works for the Wellington and Manawatu Railway (WMR) in New Zealand. Ordered in 1894, it entered service in August of that year as No. 13 and was the first narrow gauge Vauclain compound in the world. In 1908, the WMR and its locomotive fleet were purchased by New Zealand Railways Department (NZR) and incorporated into the national rail network, and, although No. 13 bore a likeness to members of the O class, it was sufficiently different that it warranted separate classification. The designation of OA was created and it was numbered OA 457. It operated for another two decades until it was withdrawn in December 1929 in Auckland. The locomotive was known to WMR staff as "The Lady".
Answer: The No. 13 locomotive was an OA class.
Question: How to make a decision boundary curve-shaped in the authors' system?
Evidence: MLP contains three layers - an input layer, an output layer and some hidden layers. Input layer receives the signal, the output layer gives a decision or prediction about the input and the computation of the MLP is conducted in the hidden layers. In our system, we use 100 layers. For weight optimization, we use Limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization algorithm.
SVM gives an optimal hyper-plane and it maximizes the margin between classes. We use Radial Basis Function (RBF) kernel in our system to make decision boundary curve-shaped. For decision function shape, we use the original one-vs-one (ovo) decision function.
NBC is based on Bayes' Theorem which gives probability of an event occurrence based on some conditions related to that event. We use Multinomial Naive Bayes Classifier with smoothing parameter equals to 0.1. A zero probability cancels the effects of all the other probabilities.
Stochastic gradient descent optimizes an objective function with suitable smoothness properties BIBREF27. It selects few examples randomly instead of whole data for each iteration. We use 'L2' regularization for reduction of overfitting.
Gradient Boosting Classifier produces a prediction model consisting of weak prediction models. Gradient boosting uses decision trees. We use 100 boosting stages in this work.
K-NN is a supervised classification and regression algorithm. It uses the neighbours of the given sample to identify its class. K determines the number of neighbours needed to be considered. We set the value of K equals to 13 in this work.
RF is an ensemble learning technique. It constructs large number of decision trees during training and then predicts the majority class. We use 500 decision trees in the forest and "entropy" function to measure the quality of a split.
We use Radial Basis Function (RBF) kernel in our system to make decision boundary curve-shaped.
Answer: The authors use Radial Basis Function (RBF) kernel in their system to make decision boundary curve-shaped.
Question: What is primary innovation that a piano delivered over a harpsichord?
Answer: Since a harpsichord plucks the strings the same way each time in response to a keystroke, it delivers the same loudness for each note. A piano on the other hand uses padded hammers to strike the strings, and allows the musician to control each strike in such a way that they have control over the dynamics of each note. This control allows pianists to express a broad range of emotions by adjusting the loudness and softness according to the feelings they want to convey to the listener.
Question: What are the colors of the sky during a sunset?
Answer: The sky is blue, pink and purple during a sunset
Question: What types of bikes are there?
Answer: There are gravel bikes, road bikes, mountain bikes, BMX bikes, recumbent bikes, unicycles, hybrid bikes, electric bikes, cruiser bikes, trail bikes, CX bikes, enduro bikes, touring bikes, fixed gear bikes, kids bikes, fat bikes, tandem bikes, folding bikes, trikes and low rider bikes.
Question: What provisional explanation do the authors give for the impact of document context?
Evidence: This effect of context on human ratings is very similar to the one reported in BIBREF5 . They find that sentences rated as ill formed out of context are improved when they are presented in their document contexts. However the mean ratings for sentences judged to be highly acceptable out of context declined when assessed in context. BIBREF5 's linear regression chart for the correlation between out-of-context and in-context acceptability judgments looks remarkably like our Fig FIGREF15 . There is, then, a striking parallel in the compression pattern that context appears to exert on human judgments for two entirely different linguistic properties.
This pattern requires an explanation. BIBREF5 suggest that adding context causes speakers to focus on broader semantic and pragmatic issues of discourse coherence, rather than simply judging syntactic well formedness (measured as naturalness) when a sentence is considered in isolation. On this view, compression of rating results from a pressure to construct a plausible interpretation for any sentence within its context.
This effect of context on human ratings is very similar to the one reported in BIBREF5 . They find that sentences rated as ill formed out of context are improved when they are presented in their document contexts.
BIBREF5 suggest that adding context causes speakers to focus on broader semantic and pragmatic issues of discourse coherence, rather than simply judging syntactic well formedness (measured as naturalness) when a sentence is considered in isolation.
Answer: The answers are shown as follows:
* adding context causes speakers to focus on broader semantic and pragmatic issues of discourse coherence
Question: How do they measure correlation between the prediction and explanation quality?
Evidence: We would expect that explanation performance should correlate with prediction performance. Since Possible-answer knowledge is primarily needed to decide if the net has enough information to answer the challenge question without guessing and relevant-variable knowledge is needed for the net to know what to query, we analyzed the network's performance on querying and answering separately. The memory network has particular difficulty learning to query relevant variables, reaching only about .5 accuracy when querying. At the same time, it learns to answer very well, reaching over .9 accuracy there. Since these two parts of the interaction are what we ask it to explain in the two modes, we find that the quality of the explanations strongly correlates with the quality of the algorithm executed by the network.
We would expect that explanation performance should correlate with prediction performance. Since Possible-answer knowledge is primarily needed to decide if the net has enough information to answer the challenge question without guessing and relevant-variable knowledge is needed for the net to know what to query, we analyzed the network's performance on querying and answering separately. The memory network has particular difficulty learning to query relevant variables, reaching only about .5 accuracy when querying. At the same time, it learns to answer very well, reaching over .9 accuracy there. Since these two parts of the interaction are what we ask it to explain in the two modes, we find that the quality of the explanations strongly correlates with the quality of the algorithm executed by the network.
Answer: They look at the performance accuracy of explanation and the prediction performance
Question: What was Freddie Mercury's real name?
Answer: Farrokh Bulsara
Question: Tell me which of these are great Irish Bands: The Hot House Flowers, The Waterboys,The Proclaimers, Deacon Blue, U2, Westlife,
Answer: Ireland has produced some of the greatest bands in the world. U2 is probably the most well known Irish band, formed in 1976. The Hot House Flowers are an Irish rock band that combine traditional Irish music with influences from soul, gospel and rock. The Boomtown Rats are another irish rock band famous for their signature song 'I don't like Mondays'.  Westlife are an irish pop group who focus on cover versions. While The Waterboys, Proclaimers and Deacon Blue are all influenced by celtic music they are in fact Scottish
Question: LinEI and NeuralEI modify the original EI by using a threshold function, whether introducing this threshold function is necessary. Do regret bounds still hold without this modification? If not, can the authors elaborate more on the technical difficulties as well as how this modification solves the issues?
Evidence: In this section, we provide a regret analysis of the NeuralEI algorithm. We first provide necessary background on the neural tangent kernel (NTK) theory, which plays an important role in our analysis. Following a recent line of research, we define the covariance between two data point x, y ∈ R d as follows:
for all i, j ∈ [T K]. Based on the above definition, we impose the following assumption on the contexts generated by the adversary and the corresponding NTK matrix H.
Assumption 1. Let H be defined in Eq(5). There exists λ 0 > 0 such that H ≥ λ 0 I. In addition, for any
Remark 1. Compared to Algorithm 1 for linear bandits, our Algorithm 2 needs an additional Assumption 1 to guarantee the convergence. The assumption that the NTK matrix is positive definite has been considered in prior work on NTK which is a mild condition and also imposed in other related works. The assumption on contexts ensures that f (x i,t ; θ 0 ) = 0 for any
The NTK technique builds a connection between deep neural networks and kernel methods. It enables us to adapt some complexity measures for kernel methods to describe the complexity of the neural network through the notation of the effective dimensions as defined in. The effective dimension d of matrix H with regularization parameter λ is defined as d = log det(I+H/λ) log(1+T K/λ) . Using these notations, we are now ready to present the second main result of the paper. Let a * (t) = argmax i∈[K] E[r i,t ] be the optimal action at round t that maximizes the expected reward, we define the expected cumulative regret after T iterations as
Then, we achieve the following upper regret bound for our Algorithm 2 by combining our EI techniques for LinEI with NTK techniques. A completed proof is provided in Supplementary Material.
Theorem 2. Under Assumption 1, set the parameters in Algorithm 2 as λ = 1 + 1/T ,
and the network width m satisfies m ≥ poly(γ, T, K, L, log(1/δ)), then with probability at least 1 − δ, the regret of Algorithm 2 is bounded as
Remark 2. The regret bound depends on the parameter β. The best choice is β = 2 that tightens the regret. Theorem 2 implies the regret of NeuralEI is on the order of Õ( d√ T ). Similar to previous results, our results require a large value of m. This is rooted in the current deep learning theory based on the neural tangent kernel.
Then, we achieve the following upper regret bound for our Algorithm 2 by combining our EI techniques for LinEI with NTK techniques.
Answer: We will explain that a threshold function is necessary and our modifications solve the issues. Due to the problem of typing math formulas in OpenReview please see our explanation in section Remark on pages 20 and 21 for details in Supplementary Material. We apologize due to this inconvenience!
Question: Given this paragraph about Berwick, what was its population during the most up to date census?
Evidence: Berwick is a borough in Columbia County, Pennsylvania, United States. It is part of Northeastern Pennsylvania and is located 28 miles (45 km) southwest of Wilkes-Barre. As of the 2020 census, Berwick had a population of 10,355. It is one of the two principal communities of the Bloomsburg–Berwick metropolitan area, which covers Columbia and Montour counties, and had a combined population of 85,562 at the 2010 census.
Answer: 10,355
Question: Why use FLOPs instead of actual training time as the metric?
Evidence: Figure 8: The Loads of each expert in 7th MoE-Layer, with 16 experts in total, in the sentence level.
Answer: Because the actual training time could be affected by the system engineering efforts on the implementation details, which are not our focus in this approach. Instead, in our experiments, we prefer to choose the computation complexity for fair comparisons. Detailed analysis (Appendix) shows that the routing pattern of DTS is relative stable, with heavy-loaded experts almost keep fixed. And we can utilize system techniques, e.g., partitioning hot experts among multiple GPUs and merging multiple cold experts onto less GPUs, for load balance between GPUs. Furthermore, we are also glad to explore the system-level optimization about how to deal with dynamic capacity of different experts in our future work.
Question: 2021 Nobel Prize was awarded to which all Journalists
Answer: Maria Ressa of Philippines & Dmitry Muratov of Russia