DataHammer/scimrc huggingface dataset:
Question: How important is pilot communication with a non-towered controlled airport?
Evidence: An airfield traffic pattern is a standard path followed by aircraft when taking off or landing while maintaining visual contact with the airfield.

At an airport, the pattern (or circuit) is a standard path for coordinating air traffic. It differs from "straight-in approaches" and "direct climb-outs" in that aircraft using a traffic pattern remain close to the airport. Patterns are usually employed at small general aviation (GA) airfields and military airbases. Many large controlled airports avoid the system unless there is GA activity as well as commercial flights. However, some kind of a pattern may be used at airports in some cases such as when an aircraft is required to go around, but this kind of pattern at controlled airports may be very different in form, shape, and purpose to the standard traffic pattern as used at GA airports.

The use of a pattern at airfields is for aviation safety. By using a consistent flight pattern, pilots will know from where to expect other air traffic and be able to see and avoid it. Pilots flying under visual flight rules (VFR) may not be separated by air traffic control, so this consistent predictable pattern is a vital way to keep things orderly. At tower-controlled airports, air traffic control (ATC) may provide traffic advisories for VFR flights on a work-load permitting basis.
Answer: Piloting into a non-towered airport in my experience can be more challenging than towered airports.  What makes it challenging is that you are not speaking to anyone in charge of the airspace.  You are broadcasting to an open communications channel and hope all the other pilots “play” by the rules.  The first best practice is to have a basic understanding of the airport, how many runways, what direction do they run, direction of the traffic pattern and traffic pattern altitude etc.?   Practice consistency as this will aid in your decision making process.  As you approach the airport you must also check the weather via AWOS or ASOS to determine the wind direction, speed and altimeter setting.  The next step is to tune into airport UNICOM to listen to other traffic in the area, note the departures and arrivals and also note other planes in the traffic pattern.  The next step is to identify yourself via the airport UNICOM channel, provide your distance to the airport and your direction from the airport.   Your approach direction will most likely determine how and where you will join the traffic pattern.  Most importantly, keep listening to the airport UNICOM and actively look for any other planes either in the traffic pattern or in the vicinity.  There are a minimum of 4 traffic pattern legs - crosswind, downwind, base and final.  Your job as a pilot is to communicate as you enter each pattern leg. It is also beneficial to communicate your intentions whether you are executing a full stop landing or a touch-n-go.  If you are doing a full-stop landing be sure to communicate when you are clear of the given runway.
Question: Why do the authors only consider ‘message’ events in the models?
Evidence: We study human-human negotiations in the buyer-seller bargaining scenario, which has been a key research area in the literature BIBREF0. In this section, we first describe our problem setup and key terminologies by discussing the dataset used. Later, we formalize our problem definition.
Dataset: For our explorations, we use the Craigslist Bargaining dataset (CB) introduced by BIBREF4. Instead of focusing on the previously studied game environments BIBREF5, BIBREF6, the dataset considers a more realistic setup: negotiating the price of products listed on Craigslist. The dataset consists of 6682 dialogues between a buyer and a seller who converse in natural language to negotiate the price of a given product (sample in Table TABREF1). In total, 1402 product ad postings were scraped from Craigslist, belonging to six categories: phones, bikes, housing, furniture, car and electronics. Each ad posting contains details such as Product Title, Category Type and a Listing Price. Moreover, a secret target price is also pre-decided for the buyer. The final price after the agreement is called the Agreed Price, which we aim to predict.
Defining the problem: Say we are provided with a product scenario $S$, a tuple: (Category, Title, Listing Price, Target Price). Define the interactions between a buyer and seller using a sequence of $n$ events $E_n:<e_{1}, e_{2}, ..., e_{n}>$, where $e_{i}$ occurs before $e_{j}$ iff $i<j$. Event $e_{i}$ is also a tuple: (Initiator, Type, Data). Initiator is either the Buyer or Seller, Type can be one of (message, offer, accept, reject or quit) and Data consists of either the corresponding natural language dialogue, offer price or can be empty. Nearly $80\%$ of events in CB dataset are of type `message', each consisting a textual message as Data. An offer is usually made and accepted at the end of each negotiation. Since the offers directly contain the agreed price (which we want to predict), we only consider `message' events in our models. Given the scenario $S$ and first $n$ events $E_n$, our problem is then to learn the function $f_{n}$: $A = f_{n}(S, E_n)$ where $A$ refers to the final agreed price between the two negotiating parties.

Since the offers directly contain the agreed price (which we want to predict), we only consider ‘message’ events in our models.
Answer: Because the offers directly contain the agreed price.
Question: How to have an evergreen garden?
Answer: In order to have an evergreen garden it's crucial to buy the right plants and plant them in the correct way in your garden. In order to choose your plants you will need to evaluate the soil and climate conditions in your garden. Please always consult planting guidance. I can strongly recommend Aucuba, Bergenia, Carex, Choisya, Cotoneaster, Erica, Escallonia, Hebe, Heuchera and Viburnum as perfect evergreen plants to put in your garden. This way you will always be surrounded by stunning green colours throughout the year with spikes of beautiful flowering in the summer.
Question: What was the result of the Emancipation Proclamation?
Answer: The Emancipation Proclamation changed the legal status of slaves in the USA from enslaved to free.
Question: For comparing the performance of their ILP-based summarization algorithm, they explored a few summarization algorithms provided by the Sumy package4, what is the common parameter required by all these algorithms?
Evidence: We considered a dataset of 100 employees, where for each employee multiple peer comments were recorded. Also, for each employee, a manual summary was generated by an HR personnel. The summaries generated by our ILP-based approach were compared with the corresponding manual summaries using the ROUGE BIBREF22 unigram score. For comparing performance of our ILP-based summarization algorithm, we explored a few summarization algorithms provided by the Sumy package. A common parameter which is required by all these algorithms is number of sentences keep in the final summary. ILP-based summarization requires a similar parameter K, which is automatically decided based on number of total candidate phrases. Assuming a sentence is equivalent to roughly 3 phrases, for Sumy algorithms, we set number of sentences parameter to the ceiling of K/3. Table TABREF51 shows average and standard deviation of ROUGE unigram f1 scores for each algorithm, over the 100 summaries. The performance of ILP-based summarization is comparable with the other algorithms, as the two sample t-test does not show statistically significant difference. Also, human evaluators preferred phrase-based summary generated by our approach to the other sentence-based summaries.
For comparing performance of our ILP-based summarization algorithm, we explored a few summarization algorithms provided by the Sumy package4. A common parameter which is required by all these algorithms is number of sentences keep in the final summary.
Answer: Number of sentences keep in the final summary.
Question: Who was Arseny Bibikov
Answer: Arseny Nikolayevich Bibikov (1873 — 1927) was a writer, film and stage actor, poet. Bibikov played in almost 50 films.
Question: What three conversational datasets are used for evaluation?
Evidence: We evaluate the proposed models on three conversationet dataset such as MojiTalk BIBREF16, PersonaChat BIBREF11, Empathetic-Dialogues BIBREF26.
We evaluate the proposed models on three conversationet dataset such as MojiTalk BIBREF16, PersonaChat BIBREF11, Empathetic-Dialogues BIBREF26.
Answer: The answers are shown as follows:
* MojiTalk 
* PersonaChat 
* Empathetic-Dialogues
Question: Tell me which is a language and which is a type of people: Malayali & Malayalam
Answer: Malayali is a people. Malayalam is the language Malayali people speak.
Question: Write a story about a cat named Rory and the adventures he gets up to while his owner, Maria, is gone at work. Write the story in the form of an hourly journal from 7am to 7 pm written by Rory, and convey that Rory loves to sleep while Maria is gone.
Answer: 7 am: There is still no food in my bowl. I have tried to point Maria in the direction of the bowl at least 8 times, and she is not noticing. I even attempted to mimic the cry of a newborn human to attract her attention, and yet she is only focused on her coffee machine. I fear she is addicted.
8 am: Belly full from breakfast. I dislike my new food, the cat on the bag scares me. I will take a nap in the sun.
9 am: Continued to nap in the sun. Had to move multiple times because the sun patch moved, very annoying. I am starting to feel higher energy as my stomach is no longer stuffed. Time to eat the remaining food in my bowl I saved for a mid morning snack
10 am: There was a bird on the porch that I scared away. It was a close call, but I showed him who is boss here
11 am: Visited my Maria's roommate at their desk. I helped them with their work, but my paws are not particularly well suited for their keyboard. Was then unceremoniously kicked out of the room
12 pm: Tried to convince the roommate to give me lunch. Instead she just sat their and ate her fish and didn't offer me any.
1 pm: Time for my afternoon nap in the hammock
2 pm: Woke up discovering I had overheated in the hammock. Hunted the laser pointer and then I noticed the bird had returned. Put him in his place again.
3 pm: Took a tour of the kitchen counters. Noticed that Maria had left the cat food partially open. Ate some to prevent it from going bad and wasting perfectly good food.
4 pm: Took a nap under Maria's bed while waiting for her to get home
5 pm: Reunited! All is well again, except for Maria still doesn't notice my food bowl
6 pm: Maria brought me a mouse to play with. I indulge her by chasing it.
7 pm: Maria finally notices the food bowl. She pulls out a new bag, without a scary cat on it. All is well in the world.
Question: Name some rap albums that were released in 2022.
Answer: Some rap albums that were released in 2022 include:

- The Forever Story by J.I.D.
- Melt My Eyez See Your Future by Denzel Curry
- King's Disease III by Nas
- Her Loss by Drake
- Mr. Morale and the Big Steppers by Kendrick Lamar
Question: Do they reduce the number of parameters in their architecture compared to other direct text-to-speech models?
Evidence: Traditional text-to-speech (TTS) systems are composed of complex pipelines BIBREF0 , these often include accoustic frontends, duration model, acoustic prediction model and vocoder models. The complexity of the TTS problem coupled with the requirement for deep domain expertise means these systems are often brittle in design and results in un-natural synthesized speech.
The architecture of our model utilizes RNN-based Seq2Seq model for generating mel spectrogram from text. The architecture is similar to that of Tacotron 2 BIBREF4 . The generated mel spetrogram can either be inverted via iterative algorithms such as Griffin Lim, or through more complicated neural vocoder networks such as a mel spectrogram conditioned Wavenet BIBREF11 .
Direct comparison of model parameters between ours and the open-source tacotron 2, our model contains 4.5 million parameters, whereas the Tacotron 2 contains around 13 million parameters with default setting. By helping our model learn attention alignment faster, we can afford to use a smaller overall model to achieve similar quality speech quality.
Traditional text-to-speech (TTS) systems are composed of complex pipelines BIBREF0 , these often include accoustic frontends, duration model, acoustic prediction model and vocoder models.
The architecture of our model utilizes RNN-based Seq2Seq model for generating mel spectrogram from text. The architecture is similar to that of Tacotron 2 BIBREF4 
Direct comparison of model parameters between ours and the open-source tacotron 2, our model contains 4.5 million parameters, whereas the Tacotron 2 contains around 13 million parameters with default setting. By helping our model learn attention alignment faster, we can afford to use a smaller overall model to achieve similar quality speech quality.
Answer: Yes.
Question: Paul McCartney credits which artist with teaching him everything he knows?
Answer: Little Richard.
Question: Is their approach the first multilingual SRL approach to combine supervision from several languages?
Evidence: The standard approach to multilingual NLP is to design a single architecture, but tune and train a separate model for each language. While this method allows for customizing the model to the particulars of each language and the available data, it also presents a problem when little data is available: extensive language-specific annotation is required. The reality is that most languages have very little annotated data for most NLP tasks.
ammar2016malopa found that using training data from multiple languages annotated with Universal Dependencies BIBREF1 , and represented using multilingual word vectors, outperformed monolingual training. Inspired by this, we apply the idea of training one model on multiple languages—which we call polyglot training—to PropBank-style semantic role labeling (SRL). We train several parsers for each language in the CoNLL 2009 dataset BIBREF0 : a traditional monolingual version, and variants which additionally incorporate supervision from English portion of the dataset. To our knowledge, this is the first multilingual SRL approach to combine supervision from several languages.
The CoNLL 2009 dataset includes seven different languages, allowing study of trends across the same. Unlike the Universal Dependencies dataset, however, the semantic label spaces are entirely language-specific, making our task more challenging. Nonetheless, the success of polyglot training in this setting demonstrates that sharing of statistical strength across languages does not depend on explicit alignment in annotation conventions, and can be done simply through parameter sharing. We show that polyglot training can result in better labeling accuracy than a monolingual parser, especially for low-resource languages. We find that even a simple combination of data is as effective as more complex kinds of polyglot training. We include a breakdown into label categories of the differences between the monolingual and polyglot models. Our findings indicate that polyglot training consistently improves label accuracy for common labels.
We train several parsers for each language in the CoNLL 2009 dataset: a traditional monolingual version, and variants which additionally incorporate supervision from English portion of the dataset. To our knowledge, this is the first multilingual SRL approach to combine supervision from several languages.
Answer: Yes.
Question: Please give me a summary of what AES-GCM is and what are its inputs and outputs.
Evidence: In cryptography, Galois/Counter Mode (GCM) is a mode of operation for symmetric-key cryptographic block ciphers which is widely adopted for its performance. GCM throughput rates for state-of-the-art, high-speed communication channels can be achieved with inexpensive hardware resources.

The GCM algorithm provides both data authenticity (integrity) and confidentiality and belongs to the class of authenticated encryption with associated data (AEAD) methods. This means that as input it takes a key K, some plaintext P, and some associated data AD; it then encrypts the plaintext using the key to produce ciphertext C, and computes an authentication tag T from the ciphertext and the associated data (which remains unencrypted). A recipient with knowledge of K, upon reception of AD, C and T, can decrypt the ciphertext to recover the plaintext P and can check the tag T to ensure that neither ciphertext nor associated data were tampered with.
Answer: AES-GCM is a mode of operation for symmetric-key cryptography. It stands for Galois/Counter Mode and is an authenticated block cipher that provides integrity and authenticity on top of confidentiality. It accepts as input a key K, a plaintest P and associated data AD, and it produces as output the ciphertext C and an authentication tag T.
Question: which of the following are ex indian prime ministers?

rajiv gandhi
rahul gandhi
manmohan singh
sonia gandhi
atal bihari vajpayee
kapil sibal
ak antony
jawaharlal nehru
Answer: Ex indian prime ministers:
rajiv gandhi
manmohan singh
atal bihari vajpayee
jawaharlal nehru
Question: Tell me about the play Die Schutzbefohlenen
Evidence: Die Schutzbefohlenen (translated as "Charges (The Supplicants)"[note 1]), is a play by Elfriede Jelinek written in 2013. She termed it a Sprachkunstwerk, a language artwork. It deals critically with the politics at the time concerning refugees. The play was first read in Hamburg on 21 September 2013. The first scenic production was in Mannheim on 23 May 2014. The first production in Austria was staged at the Burgtheater in Vienna on 28 March 2015, which was recognised internationally. Later that year, Jelinek expanded the text to reflect the changed political situation.
Answer: Die Schutzbefohlenen, translated as the supplicants is a play by Elfriede Jelinek written in 2013. She termed it a Sprachkunstwerk, a language artwork. It deals critically with the politics at the time concerning refugees. The play was first read in Hamburg on 21 September 2013. The first scenic production was in Mannheim on 23 May 2014. 

The first production in Austria was staged at the Burgtheater in Vienna on 28 March 2015, which was recognised internationally. Later that year, Jelinek expanded the text to reflect the changed political situation.
Question: What do the authors believe about their studies on the different characteristics of the encoder and decoder?
Evidence: The encoder-decoder based framework BIBREF0, BIBREF1, BIBREF2 is the dominant approach for neural machine translation (NMT) BIBREF3, BIBREF4. Although the encoder and decoder usually adopt the same model structure (RNN BIBREF5, CNN BIBREF6 or self-attention BIBREF3, BIBREF7) and the same number of layers, they perform different functionalities: the encoder extracts the hidden representations of the source sentence, and the decoder generates target tokens conditioned on the source hidden representations as well as the previous generated tokens.
While most existing works focus on the design and improvement of encoder-decoder framework for NMT BIBREF8, BIBREF6, BIBREF3, BIBREF9 as well as its detailed analyses BIBREF10, BIBREF11, BIBREF12, BIBREF13, few works concentrate on the characteristics and functionalities of the encoder and the decoder, which are valuable to understand this popular framework and improve its performance in NMT. Therefore, in this paper, we conduct a study and aim to understand the characteristics of the encoder and the decoder in NMT. We observe some interesting phenomena:
The decoder handles an easier task than the encoder. 1) We find that adding more layers to the encoder achieves larger improvements than adding more layers to the decoder. 2) We also compare the training time of the encoder and decoder by fixing the parameters of a well-trained decoder (encoder), and just update the parameters of the encoder (decoder). We found that the decoder converges faster than the encoder. These two results suggest that the decoder handles an easier task than the encoder in NMT.
The decoder is more sensitive to the input noise than the encoder. We randomly add different level of noise to the input of the encoder and decoder respectively during inference, and find that adding noise to the input of the decoder leads to better accuracy drop than that of the encoder.
We further analyze why the decoder is more sensitive by masking the previous tokens, and comparing autoregressive NMT with the non-autoregressive counterpart. We find that the preceding tokens in the decoder provide strong conditional information, which partially explain the previous two observations on the decoder.
We believe our studies on the different characteristics of the encoder and decoder will inspire the following research on the encoder-decoder framework as well as improve the performance on NMT and other encoder-decoder based tasks.
We believe our studies on the different characteristics of the encoder and decoder will inspire the following research on the encoder-decoder framework as well as improve the performance on NMT and other encoder-decoder based tasks.
Answer: The authors believe that their studies on the different characteristics of the encoder and decoder will inspire the following research on the encoder-decoder framework as well as improve the performance on NMT and other encoder-decoder based tasks.
Question: Recommend me a SCI FI fiction book
Answer: The Three body could be a good beginning