DataHammer/scimrc huggingface dataset:
Question: What six parameters were optimized with grid search?
Evidence: Among all available parameters to tune the D2V algorithm released by Gensim, six of them were selected for optimisation BIBREF14. The window_size parameter affects the size of the sliding window used to parse texts. The alpha parameter represents the learning rate of the network. The sample setting allows the model to reduce the importance given to high-frequency words. The dm parameter defines the training used architecture (PV-DM or PV-DBOW). The hs option defines whether hierarchical softmax or negative sampling is used during the training. Finally, the vector_size parameter affects the number of dimensions composing the resulting vector.
Among all available parameters to tune the D2V algorithm released by Gensim, six of them were selected for optimisation BIBREF14. The window_size parameter affects the size of the sliding window used to parse texts. The alpha parameter represents the learning rate of the network. The sample setting allows the model to reduce the importance given to high-frequency words. The dm parameter defines the training used architecture (PV-DM or PV-DBOW). The hs option defines whether hierarchical softmax or negative sampling is used during the training. Finally, the vector_size parameter affects the number of dimensions composing the resulting vector.
Answer: The answers are shown as follows:
* window_size
* alpha
* sample
* dm
* hs
* vector_size
Question: Who am I?
Answer: Ah, an existential question! Only you can answer who you are. Studying philosophy can offer guidance on the meaning of life and existence, but only you can determine how your nature, the way you were nurtured, and the decisions you've made will ultimately shape your identity.
Question: Based on the passage provided, what does Brene Brown's research focus on?
Evidence: Casandra Brené Brown (born November 18, 1965) is an American professor, lecturer, author, and podcast host. Brown is known in particular for her research on shame, vulnerability, and leadership, and for her widely viewed TEDx talk in 2010. Since then she has written six number-one New York Times bestselling books, hosts two podcasts, and has filmed a lecture for Netflix as well as a series about her latest book, Atlas of the Heart on HBO Max.
Answer: Brene Brown's research focuses on shame, vulnerability, and leadership.
Question: Classify each of the following as either characters from Star Wars or Star Trek: Jean Luc Picard, Grogu, Han Solo, Luke Skywalker, Data, Q, Darth Vader, Ahsoka, William Riker, James Kirk, Spock
Answer: Star Wars: Grogu, Han Solo, Luke Skywalker, Darth Vader, Ahsoka
Star Trek: Jean Luc Picard, Data, Q, William Riker, James Kirk, Spock
Question: Tell which of these musicians is a guitarist or a bassist:  Eddie Van Halen, Nikki Sixx, Steve Harris, Billy Sheehan, Steve Vai, Joe Satriani, Matthias Jabs, Jimmy Bain, Mick Mars, Vivian Campbell, Angus Young
Answer: Eddie Van Halen - Guitarist
Nikki Sixx - Bassist
Steve Harris - Bassist
Billy Sheehan - Bassist
Steve Vai - Guitarist
Joe Satriani - Guitarist
Matthias Jabs - Guitarist
Jimmy Bain - Bassist
Mick Mars - Guitarist
Vivian Campbell - Guitarist
Angus Young - Guitarist
Question: How did the author select the data set?
Evidence: The dev data set BIBREF19 in the competition was small hence, the train and dev sets were merged to perform 10-fold cross validation. On each fold, a model was trained and the predictions were collected on the remaining dataset. The predictions are averaged across all the folds to generalize the solution and prevent over-fitting. As described in Section SECREF6 , different combinations of feature extractors were used. After performing feature extraction, the data was then passed to various regressors Support Vector Regression, AdaBoost, RandomForestRegressor, and, BaggingRegressor of sklearn BIBREF20 . Finally, the chosen top performing models had the least error on evaluation metrics namely Pearson's Correlation Coefficient and Spearman's rank-order correlation.
The dev data set (Mohammad and BravoMarquez, 2017b) in the competition was small hence, the train and dev sets were merged to perform 10-fold cross validation. On each fold, a model was trained and the predictions were collected on the remaining dataset.
Answer: The train and dev sets were merged to perform 10-fold cross validation. On each fold, a model was trained and the predictions were collected on the remaining dataset.
Question: What motivates a chief executive officer?
Answer: Revenue and platform or product consumption and scalability motivate chief technology officers (CEOs).  It is the quickest way to achieve company exponential growth.  Strategic vision with laser focus execution focus items that drive company growth also motivate CEOs.  Having a team of world-class employees who help to deliver the strategic vision, and product platform scalability and consumption that increases revenue also motivates CEOs.
Question: What is dataset used for news-driven stock movement prediction?
Evidence: We use the public financial news dataset released by BIBREF4, which is crawled from Reuters and Bloomberg over the period from October 2006 to November 2013. We conduct our experiments on predicting the Standard & Poor’s 500 stock (S&P 500) index and its selected individual stocks, obtaining indices and prices from Yahoo Finance. Detailed statistics of the training, development and test sets are shown in Table TABREF8. We report the final results on test set after using development set to tune some hyper-parameters.
We use the public financial news dataset released by BIBREF4, which is crawled from Reuters and Bloomberg over the period from October 2006 to November 2013. We conduct our experiments on predicting the Standard & Poor’s 500 stock (S&P 500) index and its selected individual stocks, obtaining indices and prices from Yahoo Finance. Detailed statistics of the training, development and test sets are shown in Table TABREF8. We report the final results on test set after using development set to tune some hyper-parameters.
Answer: The answers are shown as follows:
* the public financial news dataset released by BIBREF4
Question: Classify the following celebrities as sports or acting personalities or both : Arnold Schwarzenegger, Brad Pitt, Kevin Durant, Shohei Ohtani, Harrison Ford, Dwayne Johnson, Jason Statham, Stephen Curry, Robin Williams, Scarlett Johansson, Tom Brady, Patrick Mahomes
Answer: Many athletes are known to do cameos in movies and a have a couple of their own sports related movies. Ignoring those few roles, I would classify the celebrities mentioned as :

Actors who were not sports personalities include Brad Pitt, Harrison Ford, Robin Williams, Scarlett Johansson.

Sports personalities who have not taken up acting as a major career (yet) are :
Kevin Durant - Acted in Thunderstuck and other movies and documentaries, but not a mainstream actor.
Stephen Curry - Did a few cameos and documentaries.
Shohei Ohtani and Patrick Mahomes only appear in sports documentaries and  tv series.
Tom Brady had a major plot role in '80 for Brady' and cameos in other movies, but he is still largely a sports personality.

Actors with successful sports background are : Arnold Schwarzenegger (body building), Dwayne Johnson (Wrestling) and Jason Statham (Diving)
Question: Tell me in which country are the following cities: Zaragoza, Albufeira, Bari, Amfissa and Targoviste.
Answer: Zaragoza is a city in Spain, Albufeira is in Portugal, Bari is located in Italy, Amfissa is part of Greece and Targoviste is in Romania.
Question: Who is considered the G.O.A.T. in basketball?
Answer: Michael Jordan, Lebron James, Kobe Bryant
Question: What are the best hiking trails near Seattle, Washington?
Answer: Washington state is famous for breathtaking nature with scenic lakes, enormous mountains, and picturesque ocean views.  Some of the best hiking trails near Seattle, Washington are: 
-  Rattlesnake Ledge Trail: in North Bend, Washington only 30 mins away from Seattle. Given how close and beautiful this trail is to the city, no wonder this is one of the most popular hiking trails in Washington. The trail is moderately challenging with 5.3 miles roundtrip and 1,459 ft of elevation. The mountain trail provides 3 levels of panoramic views of Mount Si and Mount Washington looming above RattleSnake lake, which is also a popular swimming and kayaking destination in the summer. 
- Wallace Falls via Woody Trail: a 4.9 miles out and back trail near Gold Bar, Washington, less than an hour away from Seattle. It's a scenic trail meandering along the Wallace River, with the lush vegetation and forest moss. You can see the Wallace Falls from 3 view points at Lower Falls, Middle Falls and Upper Falls. Most of the trail to Middle Falls doesn't have much elevation, the substantial elevation gain is between Middle Falls and Upper Falls. 
-  Lake 22: in Mount Baker Snoqualmie National Forest, about 2 hours drive from Seattle. Lake Twenty-Two Trail is a beautiful hike to an alpine lake through old growth forests. The trail is challenging with 6.8 miles roundtrip and 1,473 ft of elevation. When you reach the destination, you can loop around the turquoise lake and take in the enormous view of Mount Pilchuck. 
-  Colchuck Lake: in Okanogan-Wenatchee National Forest, near Leavenworth about 3 hours drive from Seattle. It features a beautiful scenery in the Alpine Lakes area of Washington state. The trail is challenging with 8.5 miles round trip and 2,349 ft of elevation. The hike can be technical with steep terrain over boulders and water streams. However, you will be rewarded with a breathtaking view of a emerald blue glacier lake and towering snow mountains. 
- From Seattle, visitors can also go to 3 beautiful National Parks with hundreds of trails such as Mount Rainier National Park (ranked 6/59), Olympic National Park (ranked 12/59), and North Cascades National Park (ranked 36/59)
Question: What is Mr. Burn's assistant name on the Simpsons?
Answer: Smithers
Question: Is Missouri a city or a state?
Answer: Both. Missouri is a state, and a city in texas
Question: What is a Security Information Event Management platform (SIEM)?
Answer: Security Information Event Management (pronounced SIM) is a combination of security information management (SIM) and security event management (SEM) technologies. It allows security operations teams to collect, parse, alert, and investigate security events. Modern SIEMs can leverage machine learning and SOAR capabilities to offer more significant detection and response capabilities.
Question: Where were the 13th South Asian Games held?
Answer: They were held in 3 cities of Nepal-Kathmandu, Pokhara & Janakpur
Question: What word embeddings were used?
Evidence: We use the word embeddings for Vietnamese that created by Kyubyong Park and Edouard Grave at al:
Kyubyong Park: In his project, he uses two methods including fastText and word2vec to generate word embeddings from wikipedia database backup dumps. His word embedding is the vector of 100 dimension and it has about 10k words.
Edouard Grave et al BIBREF11: They use fastText tool to generate word embeddings from Wikipedia. The format is the same at Kyubyong's, but their embedding is the vector of 300 dimension, and they have about 200k words
We use the word embeddings for Vietnamese that created by Kyubyong Park and Edouard Grave at al:

Kyubyong Park: In his project, he uses two methods including fastText and word2vec to generate word embeddings from wikipedia database backup dumps.
Edouard Grave et al BIBREF11: They use fastText tool to generate word embeddings from Wikipedia.
Answer: The answers are shown as follows:
* Kyubyong Park
* Edouard Grave et al BIBREF11
Question: What module does the system consist of?
Evidence: We introduce an open domain question answering system based on Chinese knowledge graph in this paper. We analyze the questions and find that most of the answers to the questions are within two hops. Therefore, we only solve the problem within two hops to reduce the complexity of the system. The system consists of a topic entity selection module, a relationship recognition module and an answer selection module. Firstly, we construct a scoring mechanism to select the core entity of the question; Then we score the relationship in the two-hop subgraph of the topic entity; Finally, we build a classier to judge whether a question is simple or complicated, so that we can choose the final relationship and generate sparql query.
The system consists of a topic entity selection module, a relationship recognition module and an answer selection module.
Answer: A topic entity selection module, a relationship recognition module and an answer selection module.
Question: Which countries are part of the five eyes alliance and what is its purpose?
Answer: The Five Eyes is an intelligence alliance between the countries of Australia, Canada, New Zealand, United Kingdom and the United States.The goal is to share intelligence between one another in one of the worlds most unilateral agreement
Question: Which psycholinguistic and basic linguistic features are used?
Evidence: Exploiting psycho-linguistic features with basic linguistic features as meta-data. The main aim is to minimize the direct dependencies on in-depth grammatical structure of the language (i.e., to support code-mixed data). We have also included emoticons, and punctuation features with it. We use the term "NLP Features" to represent it in the entire paper.
We have identified a novel combination of features which are highly effective in aggression classification when applied in addition to the features obtained from the deep learning classifier at the classification layer. We have introduced two new features in addition to the previously available features. The first one is the Emotion Sensor Feature which use a statistical model to classify the words into 7 different classes based on the sentences obtained from twitter and blogs which contain total 1,185,540 words. The second one is the collection of selected topical signal from text collected using Empath (see Table 1.).
FLOAT SELECTED: Table 1: Details of NLP features
Exploiting psycho-linguistic features with basic linguistic features as meta-data. The main aim is to minimize the direct dependencies on in-depth grammatical structure of the language (i.e., to support code-mixed data). We have also included emoticons, and punctuation features with it. We use the term "NLP Features" to represent it in the entire paper.
We have identified a novel combination of features which are highly effective in aggression classification when applied in addition to the features obtained from the deep learning classifier at the classification layer. We have introduced two new features in addition to the previously available features. The first one is the Emotion Sensor Feature which use a statistical model to classify the words into 7 different classes based on the sentences obtained from twitter and blogs which contain total 1,185,540 words. The second one is the collection of selected topical signal from text collected using Empath (see Table 1.).
FLOAT SELECTED: Table 1: Details of NLP features
Answer: Emotion Sensor Feature, Part of Speech, Punctuation, Sentiment Analysis, Empath, TF-IDF Emoticon features
Question: Why do they use a transfer learning strategy?
Evidence: Parkinson's disease patients develop different speech impairments that affect their communication capabilities. The automatic assessment of the speech of the patients allows the development of computer aided tools to support the diagnosis and the evaluation of the disease severity. This paper introduces a methodology to classify Parkinson's disease from speech in three different languages: Spanish, German, and Czech. The proposed approach considers convolutional neural networks trained with time frequency representations and a transfer learning strategy among the three languages. The transfer learning scheme aims to improve the accuracy of the models when the weights of the neural network are initialized with utterances from a different language than the used for the test set. The results suggest that the proposed strategy improves the accuracy of the models in up to 8\% when the base model used to initialize the weights of the classifier is robust enough. In addition, the results obtained after the transfer learning are in most cases more balanced in terms of specificity-sensitivity than those trained without the transfer learning strategy.
The proposed approach considers convolutional neural networks trained with time frequency representations and a transfer learning strategy among the three languages. The transfer learning scheme aims to improve the accuracy of the models when the weights of the neural network are initialized with utterances from a different language than the used for the test set.
Answer: The transfer learning scheme aims to improve the accuracy of the models when the weights of the neural network are initialized with utterances from a different language than the used for the test set.
Question: How much does the model outperform the state-of-the-art?
Evidence: Named Entity Recognition (NER) has been studied for many languages like English, German, Spanish, and others but virtually no studies have focused on the Nepali language. One key reason is the lack of an appropriate, annotated dataset. In this paper, we describe a Nepali NER dataset that we created. We discuss and compare the performance of various machine learning models on this dataset. We also propose a novel NER scheme for Nepali and show that this scheme, based on grapheme-level representations, outperforms character-level representations when combined with BiLSTM models. Our best models obtain an overall F1 score of 86.89, which is a significant improvement on previously reported performance in literature.
Our novel neural based model gained relative improvement of 33% to 50% compared to feature based SVM model and up to 10% improvement over state-of-the-art neural based models developed for languages beside Nepali.
Answer: Their novel neural based model gained up to 10% improvement over state-of-the-art neural based models developed for languages beside Nepali.
Question: How is the discriminative training formulation different from the standard ones?
Evidence: For the overlapped speech recognition problem, the conditional independence assumption in the output label streams is still made as in Equation ( 5 ). Then the cross-entropy based PIT can be transformed to sequence discriminative criterion based PIT as below,
$$\begin{split} \mathcal {J}_{\text{SEQ-PIT}}=\sum _u \min _{s^{\prime }\in \mathbf {S}} \frac{1}{N} \sum _{n\in [1,N]}-\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)}) \end{split}$$ (Eq. 44)
Different from Equation ( 7 ), the best permutation is decided by $\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)})$ , which is the sequence discriminative criterion of taking the $s^{\prime }$ -th permutation in $n$ -th output inference stream at utterance $u$ . Similar to CE-PIT, $\mathcal {J}_{\text{SEQ}}$ of all the permutations are calculated and the minimum permutation is taken to do the optimization.
For the overlapped speech recognition problem, the conditional independence assumption in the output label streams is still made as in Equation ( 5 ). Then the cross-entropy based PIT can be transformed to sequence discriminative criterion based PIT as below,

$$\begin{split} \mathcal {J}_{\text{SEQ-PIT}}=\sum _u \min _{s^{\prime }\in \mathbf {S}} \frac{1}{N} \sum _{n\in [1,N]}-\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)}) \end{split}$$ (Eq. 44)

Different from Equation ( 7 ), the best permutation is decided by $\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)})$ , which is the sequence discriminative criterion of taking the $s^{\prime }$ -th permutation in $n$ -th output inference stream at utterance $u$ . Similar to CE-PIT, $\mathcal {J}_{\text{SEQ}}$ of all the permutations are calculated and the minimum permutation is taken to do the optimization.
Answer: The answers are shown as follows:
* the best permutation is decided by $\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)})$ , which is the sequence discriminative criterion of taking the $s^{\prime }$ -th permutation in $n$ -th output inference stream at utterance $u$
Question: From the passage below, please locate the birthdate of William Harrison.
Evidence: William Henry Harrison (February 9, 1773 – April 4, 1841) was an American military officer and politician who served as the ninth president of the United States. Harrison died just 31 days after his inauguration in 1841, and had the shortest presidency in United States history. He was also the first United States president to die in office, and a brief constitutional crisis resulted as presidential succession was not then fully defined in the United States Constitution. Harrison was the last president born as a British subject in the Thirteen Colonies and was the paternal grandfather of Benjamin Harrison, the 23rd president of the United States.
Answer: William Henry Harrison was born on February 9, 1773.
Question: How should PRIME choose the training applications?What is the minimal training application subset to get the best results on all benchmarks?
Evidence: Table 15: Optimized objective values (i.e., latency in milliseconds) under zero-shot setting when the test applications include all the nine evaluated models (e.g. MobileNet (Edge, V2, V3), M4, M5, M6, t-RNN Dec, t-RNN Enc, U-Net). Lower latency is better. From left to right: the applications used to train the surrogate model in PRIME the target applications for which the accelerator is optimized for, the area constraint of the accelerator, PRIME’s (best, median) latency, and best online method’s (best, median) latency. The best accelerator configurations identified is highlighted in bold.
Answer: For the conservative surrogates trained by PRIME in Table 4, we are evaluating them on all the applications we study. A subset of the results which are complete are now shown in Appendix A.8., Table 15, Page 20, and depict that PRIME can outperform online evolutionary methods even when evaluated over all applications in a zero-shot manner. Additionally, we experimented with a few more subsets of training applications, with the goal to identify the minimal subset of applications that gives rise to good results on all benchmarks. We are running these experiments and will update the reviewer and the paper before the end of the rebuttal period.