DataHammer/scimrc huggingface dataset:
Question: What genre of music would best represent the Grateful Dead: Americana, Country, psychedelic rock, blue grass, all of the above
Answer: Depending on when one begins listening to the Grateful Dead; their music pulls from many genres of music. All of the above would be considered the most accurate.
Question: Classify each of the following as either a letter or a number
Answer: 1, a, o, 3, 9, 193, d, 23, f, d, p, 500
Question: I think as a federated setting paper, only considering one client for most experiments is unconvincing.Can authors add experiments with many clients and maybe clients from different domains?
Evidence: Two group of experiments are conducted in the case of multiple edge devices. First, the target data on the multiple edge devices come from the same target domain. Second, the target data on the multiple edge devices come from different target domains. More specifically, in the second experiments, the edge devices are divided into two groups. The devices from the same group have the target data from the same domain, while the target domains of the two groups are different.
Answer: The experiments of multiple edge devices that come from one target domain are provided in the appendix. Moreover, we will conduct the experiments of multiple edge devices that come from different domains, and then add the results in the appendix.
Question: Why does proposed term help to avoid posterior collapse?
Evidence: The immediate impact of the explicit constraint is avoiding the collapse issue ($D_{KL}=0$) by setting a non-zero positive constraint ($C\ge 0$) on the KL term ($|D_{KL}\big (q_\phi ({z}|{x}) || p({z})\big )-C|$). We experimented with a range of constraints ($C$) on the KL term and various powerful and weak decoder architectures (LSTM, GRU, and CNN), and empirically confirmed that in all cases the constraint was satisfied.
he immediate impact of the explicit constraint is avoiding the collapse issue ($D_{KL}=0$) by setting a non-zero positive constraint ($C\ge 0$) on the KL term ($|D_{KL}\big (q_\phi ({z}|{x}) || p({z})\big )-C|$).
Answer: The answers are shown as follows:
* by setting a non-zero positive constraint ($C\ge 0$) on the KL term ($|D_{KL}\big (q_\phi ({z}|{x}) || p({z})\big )-C|$)
Question: How to improve the baseline model in this paper?
Evidence: Here, we improve the baseline model with novel and specifically crafted features that rely on the medical domain and that capture details on the specific content of an article. As shown in Figure 3 , medical model features, the bio-medical entities, have been extracted from the free text only, exploiting advanced NLP techniques and using domain dictionaries.
In details, we newly define and extract from the dataset the following novel features:
The idea of considering infoboxes is not novel: for example, in BIBREF7 the authors noticed that the presence of an infobox is a characteristic featured by good articles. However, in the specific case of the Medicine Portal, the presence of an infobox does not seem strictly related to the quality class the article belongs to (according to the manual labelling). Indeed, it is recurrent that articles, spanning all classes, have an infobox, containing a schematic synthesis of the article. In particular, pages with descriptions of diseases usually have an infobox with the medical standard code of the disease (i.e., IDC-9 and IDC-10), as in Figure 2 .
As done for the baseline, also the first two features of the medical model have been extracted with ad hoc Python scripts, extracting HTML structures and excerpts of the textual content within the MediaWiki tags. For their extraction of the bio-medical entities, we consider the textual part of the article only, obtained after removing the MediaWiki tags, and we apply a NLP analysis, which is presented in Section "Bio-medical entities" .
Here, we improve the baseline model with novel and specifically crafted features that rely on the medical domain and that capture details on the specific content of an article.
Answer: The authors improve the baseline model with novel and specifically crafted features that rely on the medical domain and that capture details on the specific content of an article.
Question: Is Dataset 1 monolingual dataset?
Evidence: This dataset was created by BIBREF8 . The tweets were downloaded from Twitter using #sarcasm as a marker for sarcastic tweets. It is a monolingual English dataset which consists of a balanced distribution of 50,000 sarcastic tweets and 50,000 non-sarcastic tweets.
Since sarcastic tweets are less frequently used BIBREF8 , we also need to investigate the robustness of the selected features and the model trained on these features on an imbalanced dataset. To this end, we used another English dataset from BIBREF8 . It consists of 25,000 sarcastic tweets and 75,000 non-sarcastic tweets.
We have obtained this dataset from The Sarcasm Detector. It contains 120,000 tweets, out of which 20,000 are sarcastic and 100,000 are non-sarcastic. We randomly sampled 10,000 sarcastic and 20,000 non-sarcastic tweets from the dataset. Visualization of both the original and subset data show similar characteristics.
A two-step methodology has been employed in filtering the datasets used in our experiments. Firstly, we identified and removed all the “user", “URL" and “hashtag" references present in the tweets using efficient regular expressions. Special emphasis was given to this step to avoid traces of hashtags, which might trigger the models to provide biased results. Secondly, we used NLTK Twitter Tokenizer to ensure proper tokenization of words along with special symbols and emoticons. Since our deep CNNs extract contextual information present in tweets, we include emoticons as part of the vocabulary. This enables the emoticons to hold a place in the word embedding space and aid in providing information about the emotions present in the sentence.
It is a monolingual English dataset which consists of a balanced distribution of 50,000 sarcastic tweets and 50,000 non-sarcastic tweets.
Answer: Yes.
Question: How to collect the out-of-scope queries?
Evidence: Out-of-scope queries were collected in two ways. First, using worker mistakes: queries written for one of the 150 intents that did not actually match any of the intents. Second, using scoping and scenario tasks with prompts based on topic areas found on Quora, Wikipedia, and elsewhere. To help ensure the richness of this additional out-of-scope data, each of these task prompts contributed to at most four queries. Since we use the same crowdsourcing method for collecting out-of-scope data, these queries are similar in style to their in-scope counterparts.
The out-of-scope data is difficult to collect, requiring expert knowledge of the in-scope intents to painstakingly ensure that no out-of-scope query sample is mistakenly labeled as in-scope (and vice versa). Indeed, roughly only 69% of queries collected with prompts targeting out-of-scope yielded out-of-scope queries. Of the 1,200 out-of-scope queries collected, 100 are used for validation and 100 are used for training, leaving 1,000 for testing.
First, using worker mistakes: queries written for one of the 150 intents that did not actually match any of the intents. Second, using scoping and scenario tasks with prompts based on topic areas found on Quora, Wikipedia, and elsewhere.
Answer: First, using worker mistakes: queries written for one of the 150 intents that did not actually match any of the intents. Second, using scoping and scenario tasks with prompts based on topic areas found on Quora, Wikipedia, and elsewhere.
Question: What is the contribution of the intrinsic analysis in this paper?
Evidence: We present a comprehensive comparison study of the existing corpora for selection-based question answering. Our intrinsic analysis provides a better understanding of the uniqueness or similarity between these corpora. Our extrinsic analysis shows the strength or weakness of combining these corpora together for statistical learning. Additionally, we create a silver-standard dataset for answer retrieval and triggering, which will be publicly available. In the future, we will explore different ways of improving the quality of our silver-standard datasets by fine-tuning the hyper-parameters.
Our intrinsic analysis provides a better understanding of the uniqueness or similarity between these corpora.
Answer: The intrinsic analysis provides a better understanding of the uniqueness or similarity between these corpora.
Question: Why was America called the New World?
Evidence: The term New World is often used to mean the majority of Earth's Western Hemisphere, specifically the Americas. The term gained prominence in the early 16th century, during Europe's Age of Discovery, shortly after the Italian explorer Amerigo Vespucci concluded that America (now often called the Americas) represented a new continent, and subsequently published his findings in a pamphlet he titled Latin: Mundus Novus. This realization expanded the geographical horizon of classical European geographers, who had thought the world consisted of Africa, Europe, and Asia, collectively now referred to as the Old World, or Afro-Eurasia. The Americas were thus also referred to as "the fourth part of the world".
Answer: In the past, the major civilizations were unaware of the existence of America. Since their world view consisted of Europe, Africa and Asia, the finding of an entire continent of the Americas was a major geographical perspective to the people of the era. They referred to America as the New World.
Question: 8 Simple Ways to Adopt a Healthy and sustainable Eating Pattern
Answer: Include more whole foods in Diet
Try to avoid crash diet
Get Professional help in beginning
Include and surround yourself with healthy foods
Include healthy snack and carry it with you
Plan alternative before going out
Monitor yourself your progress and be patient
Break old Habits one at a time
Question: What are the best ways to decrease sound resonance in a room?
Answer: The most impactful ways to decrease the sound resonance in a room would be to concentrate on a few significant points. The most impactful and easiest change is the source of the sound that is having resonance. A more suitable location not orthogonally facing a wall will decrease echo. Sound absorption is paramount to creating a space that does not meaningfully reflect sound waves in a way that causes resonance. Focus on corners and large flat walls to prevent resonance. The material and shape can distort the sound waves enough to prevent any audible artifacts from the resonance.
Question: What was the former name of The Washington Commanders football team?
Evidence: The Washington Commanders are a professional American football team based in the Washington metropolitan area. Formerly known as the Washington Redskins, the team competes in the National Football League (NFL) as a member of the National Football Conference (NFC) East division. The team plays its home games at FedExField in Landover, Maryland; its headquarters and training facility are in Ashburn, Virginia. The team has played more than 1,000 games and is one of only five in the NFL with more than 600 total wins. Washington was among the first NFL franchises with a fight song, "Hail to the Commanders", which is played by their marching band after every touchdown scored by the team at home. The franchise is valued by Forbes at US$5.6 billion, making them the league's sixth-most valuable team as of 2022.
Answer: The Washington Commanders was formally known as The Washington Redskins, the team competes in the National Football League (NFL) as a member of the National Football Conference (NFC) East division.
Question: What is the best recipe for moonshine
Answer: Take 5 gallons of water to 175 degree Fahrenheit, and once it reaches temperature turn off heat.  Add seven and a half pounds of cracked corn or milled feed corn, and stir every 5 minutes until the temperature reaches 155 degrees.  At that point add two and a half pounds of barley malt while stirring every 15 minutes.  Wait until the temperature gets down to 75 degrees, and this may take a while, overnight in some cases.  Once at or below 75 degrees add two tablespoons of brewers yeast and stir.  Let sit for 3 weeks.  Distill at this point and enjoy the results.
Question: Identify which instrument is string or percussion: Jhallari, Chitarra battente
Answer: Chitarra battente is string, Jhallari is percussion.
Question: Why is India a major outsourcing partner for Information technology services?
Answer: India ranks as the number one country in the world in providing technical support and Information technology related services. India has more than 50% of the global IT outsourcing share (https://idapgroup.com/blog/it-outsourcing-to-india/#:~:text=Indian%20IT%20in%20Numbers%20%26%20Facts&text=Holds%2055%25%20of%20global%20IT,countries%20all%20over%20the%20globe.) 

India has a large educated young workforce that forms the core engine for driving the growth of IT services from India. This workforce is trained in skills in demand, namely the Information technology related topics, from the many universities spread across the country. 

English is a language commonly used in India. There is a huge workforce that is well trained and proficient in English. This is a huge advantage when it comes to providing services to global customers who are looking to use a team that is able to communicate in English.

India is a developing country. The cost of living and the salary scale for employees is low compared the salary for similar employees in developed countries such as the United States of America, Great Britain, Australia, France, etc. The difference in salary levels provides a natural cost arbitrage that provides a compelling reason when a company is exploring to outsource.

The combination of the above factors namely availability of a trained workforce that is able to communicate in English and the arbitrage in salary costs makes India an attractive outsourcing partner for many businesses in the developed countries.
Question: What does the quantity on the columns (5, 10, ..., 30) represent? Please clarify that. The Appendix G does not describe the experiment used to create Figure 7. That would have been useful.
Evidence: Section 2 speculated that the distribution of parameter ranks differ sufficiently for the detection of malicious and benign nodes. We validate this hypothesis in Figure by illustrating the difference between the benign nodes and malicious nodes in terms of the mean of gradients' rankings and the standard deviation of gradients' ranking.
It can be observed from Figure that, under Gaussian and Label flipping attacks, the average rankings of malicious nodes are of a similar distribution to benign nodes. It is problematic for distinguishing between the two types of nodes, if only average ranking information is used. On the other hand, Figure displays a larger separation of distributions for the standard deviation of ranking. It is noted that all 4 attacks observe a convergence of the distributions as the number of malicious nodes increase, increasing the difficulty of defense for both MANDERA and all other defenses. However, the likelihood of an attacker controlling increasingly large numbers of malicious nodes also decrease.
On the other hand, Figure 3 displays a larger separation of distributions for the standard deviation of ranking. It is noted that all 4 attacks observe a convergence of the distributions as the number of malicious nodes increase, increasing the difficulty of defense for both MANDERA and all other defenses.****Figure 7: Model Loss at each epoch of training, each line of the curve represents a different defense against the attacks (GA: Gaussian attack; ZG: Zero-gradient attack; SF: Sign-flipping; and LF: Label-flipping).
Answer: We apology for the confusion. The number on each column represents the number of malicious nodes. The relevant appendix section is now H. More descriptions will be added.
Question: What is the holiday of Passover about?
Answer: Passover is a Jewish holiday which commemorates the events of the book of Exodus, when Israelites fled oppression in Ancient Egypt
Question: Give me a bulleted list of the last 10 Major League Baseball world series champions.
Answer: 2022 Houston Astros
2021 Atlanta Braves
2020 Los Angeles Dodgers
2019 Washington Nationals
2018 Boston Red Sox
2017 Houston Astros
2016 Chicago Cubs
2015 Kansas City Royals
2014 San Francisco Giants
2013 Boston Red Sox
Question: What episodes did William Gibson write for "The X-Files"?
Answer: William Gibson wrote season 5's "Kill Switch" and season 7's "First Person Shooter."
Question: What is an adjective?
Answer: An adjective is a describing word for a noun or proper noun.  A noun is a person, place, or thing, and a proper noun is the name of person.  An adjective would be as describing word like beautiful, smart, or fun.  Examples using these adjectives are the following: she is beautiful person, bounce house is fun place, or sally is a very smart person.
Question: Name the last five California governors, including the current one
Answer: Gavin Newsom, Jerry Brown, Arnold, Gray Davis, Pete Wilson
Question: Which NFL team has the most Super Bowl championship wins?
Answer: The Pittsburgh Steelers and the New England Patriots are tied for the most Super Bowl championship wins. Both teams have won 6 Super Bowl championships. The Steelers were the first team to achieve this in 2008, while the Patriots earned their sixth championship win in 2018.
Question: Some assumptions are unclear if they are mild or why are they assumed in the first place? For instance, assuming fixed eigenvalues looks like a rather strange assumption at first glance, and authors do not provide any intuition why this is assumed and how restrictive it is. To me, that assumption is an artifact for the proof techniques, which undermines the contributions of the paper.
Evidence: Then we consider the random vector formed by the measurements x = (l1, . . . , ln, t1, . . . , tn), where li and ti are the length and temperature of object i, respectively. This results in a block-structured covariance matrix. When we measure more objects, the size of the covariance matrix tends to infinity. Second, the motivation came from (Nakkiran et al., 2021). (Nakkiran et al., 2021) observed empirically in their Figure 2 that when the covariance matrix has a block structure (specifically, there are only two fixed different eigenvalues 10 and 1), the expected excess risk exhibits multiple descent. We quantitatively studied this observation and obtained the related formulae.****Thus there is no sample-wise double or multiple descent. This provides a theoretical proof of a phenomenon observed in (Nakkiran et al., 2021) that optimal regularization can mitigate double descent for anisotropic data. Note that without regularization, there will be a blow-up in expected excess risk when n = d (the linear model exactly interpolates the data) and therefore, there is no samplewise descent across the under- and over-parameterized regimes.
Answer: Sorry for the confusion. First, in practice, there are natural random variables that satisfy our assumption. For example, assume that we want to use machine A to measure the length of several objects and use machine B to measure their temperature. The measured lengths and temperatures follow i.i.d. Gaussian distribution. However, the variance of measurement of machine A is different from that of machine B. Then we consider the random vector formed by the measurements (length of object 1, length of object 2, ..., length of object n, temperature of object 1, temperature of object 2, ..., temperature of object n). This results in a block-structured covariance matrix. When we measure more objects, the size of the covariance matrix tends to infinity. Second, the motivation came from (Nakkiran et al., 2021). Nakkiran et al. (2021) observed  empirically in their Figure 2 that when the covariance matrix has a block structure (specifically, there are only two fixed different eigenvalues 10 and 1), the expected excess risk exhibits multiple descent. We quantitatively studied this observation and obtained the related formulae. We stated this in the third item in Section 1.2. We further clarified in the revision.
Question: Give me three spicy authentic Chinese food dishes
Answer: There are many spicy Chinese food out there, among those the following are the local favorite dishes.
1. Chongqing Spicy Chicken
2. Kung Pao Chicken
3. Fish-Flavored Shredded Pork
Question: What is a protractor?
Answer: A protractor is a mathematical instrument/device that can be used to help calculate the angle
Question: What is NMT?
Evidence: Accurate grapheme-to-phoneme conversion (g2p) is important for any application that depends on the sometimes inconsistent relationship between spoken and written language. Most prominently, this includes text-to-speech and automatic speech recognition. Most work on g2p has focused on a few languages for which extensive pronunciation data is available BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Most languages lack these resources. However, a low resource language's writing system is likely to be similar to the writing systems of languages that do have sufficient pronunciation data. Therefore g2p may be possible for low resource languages if this high resource data can be properly utilized.
We attempt to leverage high resource data by treating g2p as a multisource neural machine translation (NMT) problem. The source sequences for our system are words in the standard orthography in any language. The target sequences are the corresponding representation in the International Phonetic Alphabet (IPA). Our results show that the parameters learned by the shared encoder–decoder are able to exploit the orthographic and phonemic similarities between the various languages in our data.
We attempt to leverage high resource data by treating g2p as a multisource neural machine translation (NMT) problem.
Answer: It is short for Neural machine translation.
Question: Jill, a 60-year-old personal training client, wants to improve her hip flexibility. She
repeatedly bounces up and down, trying to touch her toes from a standing position. Will hip
flexibility be the expected outcome of this exercise? Is her technique appropriate? What
modifications would you make, if any?
Answer: In practice, she may find some improvements in her hip flexibility, but certainly this is not the right, nor optimal, technique to improve her hip flexibility. She risks injuring herself with this kind of movement. This is because every tendon has a stretch reflex, and when triggered, will cause the muscle connected to it to contract; for example, when the patellar tendon underneath your knee is hit by your doctor during your yearly checkup, your quads contract involuntarily and your leg kicks up. When you are stretching, you are elongating the muscles. If you bounce while stretching, you can activate your tendon’s stretch reflex, causing your muscle to try and contract while also elongating it, which may tear the muscle or even detach the tendon from the bone in extreme cases. I would recommend her to not bounce during toe touches, and focus on keeping the motion static.
Question: classify the following as sports vs other activities: cricket, talking, writing, running, dancing, football, golf, construction, plumbing, swimming, paragliding, racing, getting late
Answer: sports: cricket, running, football, golf, swimming, paragliding, racing
other activities: talking, writing, dancing, construction, plumbing, getting late