DataHammer/scimrc huggingface dataset:
Question: What are the steps to care for a houseplant?
Answer: First, be sure to provide water, sunlight and humidity levels necessary for the variety of plant you have. For example, succulent-type plants may require less frequent waterings than their green-leafy counterparts. Research your new plants' preferred sunlight, water, and humidity requirements so you can provide the best care. Second, be sure to review your newly acquired houseplant for any pests before introducing them into your home. You’ll want to check the leaves for mealy bugs, spider mites, etc. and the soil for pests such as fungus gnats. If you find any of these be sure to quarantine your new plant away from your current plants and treat with a pesticide of your choosing. Many houseplant owners use neem oil to treat pests. Third, upsize or repot your plant only when it is ready. Your new plant likely will be happy for a while in its current nursery (plastic) container that it came home from the shop in. You can choose to upsize the pot once you notice signs of the plant being root bound (the roots take up a majority of the pot) such as water draining from the pot quickly and the soil not becoming saturated or the plant slows its growth of new leaves (unless its the winter season at which time plants naturally slow or stop new growth). Spring time is the best time to repot your plants.
Question: Give me a list of amazing noise cancelling headphones in 2023
Answer: 1. Sony WH-1000XM5 - Amazing for great music and also for taking office video calls
2. Apple AirPods Max - Ideal for apple fanatics
3. Sennheiser Momentum 4 Wireless - Great brand and product
4. Sony WH-1000XM4 - yes previous version are equally good and much more affordable on your pocket.
5. Bowers & Wilkins PX7 S2
6. Bose QuietComfort 45 - State of the art noise cancellation
7. Anker SoundCore Space Q45 - Great price and quality.
Question: As a leader, how do you build an environment of trust?
Answer: In order to build an environment of trust, a leader should should take a bottom-up approach to identify and empathize on efforts being taken by the team in the ground, and be able to relate to it. One of the main pillars of strong leadership is built on trust, which can flourish over a period of time by nurturing the team, keeping faith on them and holding their hands during difficult times.
Question: If you were given 1 million dollars in your fidelity account provide a list of which stocks you would buy
Answer: 1. Apple
2. Google
3. Microsoft 
4. Amazon
5. NVIDIA 
6. Netflix
7. PayPal
8. Disney
9. Meta
10. Tesla
Question: Polly, a 51-year-old female, visits your fitness facility. She is dissatisfied with her body and is responding to a series of ads your facility uses to attract people interested in weight loss. Can we think of ways she can become more accepting of her body?
Answer: Instead of jumping to intense workout regimens or strict diets, we can coax her to focus more on what her body can accomplish physically versus just how it looks. We wish to encourage her to accept her body and cherish her progress in her athletic endeavors, e.g. if she is consistently performing resistance exercises, remind her that her body can now lift much more weight than when she first started. She can focus more on that, instead of purely a number on a scale. Moreover, we can also dig into where her body insecurities came from, and take steps so that she can work through this trauma. Suggestions include renaming a specific disliked body part, focusing on the great parts of her own body, and appreciating the inherent strength and functionality of her body.
Question: What are ways to calm myself down when I get upset?
Answer: If you become upset or agitated, a great way to calm down is take a deep breath, like a “dragon breath.” Breathe in as deeply as you can and then breathe out slowly. Repeat this several times. You can also lay on the floor for several minutes. Laying on the floor can be particularly grounding. Another way to relax when you get upset is to go outside and look at the sky or a body of water if you can get to one. Look for a flying bird, or focus on a small flower or piece of grass. A long walk can also help calm you down.
Question: What is the defination of the σ?
Evidence: The one-hidden layer convolutional neural network with a binary activation function is realized as
where σ(•) is the step function,
Z ∈ R m×n is constructed by deividing the input data, e.g. an image, into m patches with size n, and w ∈ R n and v ∈ R m are the trainable weights, i.e. kernels, in the first and second layers, respectively). We assume that Z is generated by a continuous distribution P (Z).
Due to the property of the step function, the network is invariant under the scale transformation for w, w → kw (k > 0).
Using the loss for each sample, (y(v, w, Z); y * ) with y * being the labels generated by the true distribution q(y * |Z), we can express the population loss function as L(v, w; y * ) = E Z [ (y(v, w, Z); y * )] .
(3)
σ(x) = 1 x > 0, 0 x ≤ 0,
Answer: Thank you very much for the comment.  To make the definition of σ clear, we have added Eq.(2) in Sec.2.1.
Question: How does a transistor amplify a signal?
Answer: A transistor is a constructed from materials that behave as semiconductors--which is to say that these materials conduct electrical current but only when certain conditions are met. By combining these materials into a device called a transistor we are able to create an electronic component that will only conduct electricity when a small electric current is applied to the so-called Base pin of the transistor.

One obvious use of such behavior is to use the transistor as a switch--by applying or removing current to the base pin we are able to control whether the transistor conducts electricity via its collector and emitter pins. When no current is present at the Base pin the transistor will block electrical transmission through its emitter and collector pins.

Generally speaking, the electrical current required to control the transistor at the base pin is very small. Much larger currents can be transferred through the device through the collector and emitter pins. Thus, in effect, a transistor uses a very small input current to control the flow of a much larger current.

An audio signal, for example, is transmitted as a very low power electrical current--in it's original form it is too small to be able to drive a loudspeaker to make sounds that are audible to the human ear. To amplify this signal so that is usable for audio playback we can apply the audio signal to a transistor's base pin--this will allow a much larger current to flow between the emitter and collector pins and through the transistor. Since the flow of this large current will mirror the much smaller current of the audio signal we will have effectively created a much stronger version of the original input signal.
Question: Which NBA team has the longest playoff drought?
Answer: The Sacramento Kings have the longest playoff drought in the NBA. The Sacramento Kings have a 16-year playoff drought and the team last appeared in the post-season in 2006. In that series, the San Antonio Spurs eliminated the Sacramento Kings in the first round after winning the series 4-2.
Question: Does Belgrandia varica live in the ocean?
Evidence: Belgrandia varica is a species of minute freshwater snail with an operculum, an aquatic gastropod mollusc or micromollusc in the family Hydrobiidae.
Answer: No, Belgrandia varica lives in freshwater.
Question: List all the persons in the text in alphabetical order using their surname: 

At acute times where there is a lot of pressure crossing the Channel, whether that’s on tunnel or ferries, then I think there’s always going to be a backup. I just urge everybody to be a bit patient while the ferry companies work their way through the backlog.”

Later, speaking on the Laura Kuenssberg programme on BBC One, Braverman denied the situation at Dover would repeat itself and blamed “bad weather”.

But her comments variously attracted ire and ridicule. The Liberal Democrats’ home affairs spokesperson, Alistair Carmichael, said: “These comments show Suella Braverman is in complete denial about the impact of the Conservative government’s botched deal with Europe on our borders. For Conservative ministers like Braverman, it is always someone else’s fault.

“Businesses and travellers are being tied up in reams of red tape but ministers are refusing to lift a finger. It shows the Conservative party is out of touch, out of excuses and should be out of power.”

The former Conservative cabinet minister David Gauke, who was stripped of the Tory whip for rebelling in a Brexit vote, tweeted: “If we were in the EU, the French would not need to do individual passport checks. If no individual passport checks, the process at Dover would be quicker. The Dover queues are, therefore, partly caused by Brexit. Not a contentious point, surely?”

Caroline Lucas, the Green party MP, described Braverman as being “on another planet as usual”. She said that the home secretary’s comments regarding no Brexit link contradicted those expressed by the port’s chief executive. In an interview with the Observer a year ago, Doug Bannister, admitted Brexit was causing longer processing times at the border.
Answer: - Doug Bannister
- Suella Braverman
- Alistair Carmichael
- David Gauke
- Laura Kuenssberg 
- Caroline Lucas
Question: Which characters belong to DC or Marvel Universe? Catwoman, Black Cat
Answer: Catwoman is DC, Black Cat is Marvel
Question: What are the tasks in the multitask learning setup?
Evidence: Within this work, we particularly target emotional valence as the primary task, as it has been shown to be the most challenging emotional dimension for acoustic analyses in a number of studies BIBREF10 , BIBREF11 . Apart from solely targeting valence classification, we further investigate the principle of multitask learning. In multitask learning, a set of related tasks are learned (e.g., emotional activation), along with a primary task (e.g., emotional valence); both tasks share parts of the network topology and are hence jointly trained, as depicted in Figure FIGREF4 . It is expected that data for the secondary task models information, which would also be discriminative in learning the primary task. In fact, this approach has been shown to improve generalizability across corpora BIBREF12 .
In multitask learning, a set of related tasks are learned (e.g., emotional activation), along with a primary task (e.g., emotional valence); both tasks share parts of the network topology and are hence jointly trained, as depicted in Figure FIGREF4 .
Answer: The answers are shown as follows:
* set of related tasks are learned (e.g., emotional activation)
* primary task (e.g., emotional valence)
Question: What format does Delta Lake use to store data?
Answer: Delta Lake uses versioned Parquet files to store your data in your cloud storage. Apart from the versions, Delta Lake also stores a transaction log to keep track of all the commits made to the table or blob store directory to provide ACID transactions.
Question: Lauren is a 32-year-old women who weighs 166 lbs and has never participated in resistance training. She participated in an initial fitness assessment and wants your advice about starting a muscular fitness program at your exercise facility. Design a resistance training routine for Lauren to help her achieve moderate gains in both muscular strength and endurance.
Answer: First, we design the resistance training routine according to the FITT principle: she should be exercising at least 2 non-consecutive days per week, 1-3 sets per exercise, 8-10 exercises, resting 30 seconds to 2 minutes between exercises, and targeting large muscle groups with her exercises. When she first starts, we’re going to begin with more weight training machines versus free weights, as she’s a beginner, and free weights may be more dangerous. We’ll prescribe machines such as a horizontal chest press, lat pull-down, leg press. As she progresses with her experience lifting weights, we will prescribe more and more free weight exercises under the careful guidance of a spotter, such as squat, deadlift, bench press, overhead press, barbell rows, etc. To achieve gains in both muscular strength and endurance, we’ll suggest alternating days targeted for hypertrophy (1-2 sets, 10 exercises, low resting times e.g. 30 seconds, high number of repetitions, and lower weight) versus muscular strength (1-4 sets, 8 exercises, longer resting times e.g. 2 minutes, lower reps and high weight).
Question: From the passage provided, extract the total amount of milk produced by Brazil in 2018
Evidence: Brazil is the world's largest exporter of chicken meat: 3.77 million tonnes in 2019. The country is the holder of the second largest herd of cattle in the world, 22.2% of the world herd. The country was the second largest producer of beef in 2019, responsible for 15.4% of global production. It was also the 3rd largest world producer of milk in 2018. This year, the country produced 35.1 billion liters. In 2019, Brazil was the 4th largest pork producer in the world, with almost 4 million tonnes.
Answer: Brazil produced 35.1 billion litres of milk in 2018
Question: What 5-letter word spelt in all capital letters can be read the exact same upside down?
Answer: SWIMS
Question: How do they obtain word lattices from words?
Evidence: Word Lattice
As shown in Figure FIGREF4 , a word lattice is a directed graph INLINEFORM0 , where INLINEFORM1 represents a node set and INLINEFORM2 represents a edge set. For a sentence in Chinese, which is a sequence of Chinese characters INLINEFORM3 , all of its possible substrings that can be considered as words are treated as vertexes, i.e. INLINEFORM4 . Then, all neighbor words are connected by directed edges according to their positions in the original sentence, i.e. INLINEFORM5 .
Word Lattice
As shown in Figure FIGREF4 , a word lattice is a directed graph INLINEFORM0 , where INLINEFORM1 represents a node set and INLINEFORM2 represents a edge set. For a sentence in Chinese, which is a sequence of Chinese characters INLINEFORM3 , all of its possible substrings that can be considered as words are treated as vertexes, i.e. INLINEFORM4 . Then, all neighbor words are connected by directed edges according to their positions in the original sentence, i.e. INLINEFORM5 .
Answer: By considering words as vertices and generating directed edges between neighboring words within a sentence
Question: Based on this questio about Kingdom Hearts, what company published it?
Evidence: Kingdom Hearts is a fantasy action role-playing game franchise created by Japanese game designers Tetsuya Nomura and Shinji Hashimoto, being developed and published by Square Enix (originally by Square). It is a collaboration between Square Enix and The Walt Disney Company and is under the leadership of Nomura, a longtime Square Enix employee.
Answer: Square Enix
Question: How big is performance improvement proposed methods are used?
Evidence: The performance of the base model described in the previous section is shown in the first row of Table TABREF8 for the Nematus cs-en ($\bar{cs}$), FB MT system cs-en (cs) and es-en (es), sequence autoencoder (seq2seq), and the average of the adversarial sets (avg). We also included the results for the ensemble model, which combines the decisions of five separate baseline models that differ in batch order, initialization, and dropout masking. We can see that, similar to the case in computer vision BIBREF4, the adversarial examples seem to stem from fundamental properties of the neural networks and ensembling helps only a little.
FLOAT SELECTED: Table 3: Accuracy over clean and adversarial test sets. Note that data augmentation and logit pairing loss decrease accuracy on clean test sets and increase accuracy on the adversarial test sets.
The performance of the base model described in the previous section is shown in the first row of Table TABREF8 for the Nematus cs-en ($\bar{cs}$), FB MT system cs-en (cs) and es-en (es), sequence autoencoder (seq2seq), and the average of the adversarial sets (avg).
FLOAT SELECTED: Table 3: Accuracy over clean and adversarial test sets. Note that data augmentation and logit pairing loss decrease accuracy on clean test sets and increase accuracy on the adversarial test sets.
Answer: Data augmentation (es)  improved Adv es by 20% comparing to baseline 
Data augmentation (cs) improved Adv cs by 16.5% comparing to baseline
Data augmentation (cs+es) improved both Adv cs and Adv es by at least 10% comparing to baseline 
All models show improvements over adversarial sets
Question: What is ice cream
Answer: Ice cream is a frozen dessert, typically made from milk or cream and flavored with a sweetener, either sugar or an alternative, and a spice, such as cocoa or vanilla, or with fruit such as strawberries or peaches. It can also be made by whisking a flavored cream base and liquid nitrogen together. Food coloring is sometimes added, in addition to stabilizers. The mixture is cooled below the freezing point of water and stirred to incorporate air spaces and to prevent detectable ice crystals from forming. The result is a smooth, semi-solid foam that is solid at very low temperatures (below 2 °C or 35 °F). It becomes more malleable as its temperature increases.
Question: Should I use a dynamic microphone or a condenser microphone for recording a podcast? What about streaming on twitch or making youtube videos? Should I ever use a ribbon microphone?
Answer: The most important factor when deciding on a microphone is the environment where you will be recording. Each microphone technology was developed for a certain kind of environment.

Condenser microphones like the Blue Yeti are designed to record their entire environment with the utmost clarity. If your environment is noisy then the microphone audio will be noisy. But if the environment has been acoustically treated then the recording will sound detailed and clear. 

Dynamic microphones like the Shure SM7B are designed to record sounds spoken within a 18 inches of the front of the microphone. All other sounds will be quieter and less detailed. If the environment is noisy then the microphone audio will tend to mute out sound that comes from off-axis. If the environment is quiet then the audio will sound good, but only if the person or instrument being recorded is within 18 inches of the microphone.

A ribbon microphone is designed to capture extremely loud percussion sounds, usually from drums or brass instruments. They are not suitable for recording human voices as they tend to emphasize frequencies not usually associated with the human voice.
Question: How did Rotisserie baseball become popular?
Evidence: Rotisserie baseball, nicknamed roto, proved to be popular despite the difficulties of compiling statistics by hand, which was an early drawback to participation. The traditional statistics used in early rotisserie leagues were often chosen because they were easy to compile from newspaper box scores or weekly information published in USA Today.[citation needed] Okrent credits the idea's rapid spread to the fact that the initial league was created by sports journalists, telling Vanity Fair in 2008 that "most of us in the league were in the media, and we got a lot of press coverage that first season. The second season, there were rotisserie leagues in every Major League press box." According to Okrent, rotisserie baseball afforded sportswriters the opportunity to write about baseball-related material during the 1981 Major League Baseball strike, saying "the writers who were covering baseball had nothing to write about, so they began writing about the teams they had assembled in their own leagues. And that was what popularized it and spread it around very, very widely."
Answer: Rotisserie baseball became popular because it allowed sportswriters to write about baseball during the 1981 Major League Baseball strike.
Question: How do I get to Juneau, Alaska?
Answer: You really only have two options, by boat or by plane. No roads lead directly to Juneau, Alaska, due to the surrounding geography. Many cruise ships visit Juneau during summer, which is how it receives most of its visitors.
Question: What metrics are used to benchmark the results?
Evidence: Table TABREF31 shows the F-score, the Area Under the ROC Curve (AUC) and the mean accuracy (ACC) of the proposed models when different inputs are available. $TT$ refers to the tweet text, $IT$ to the image text and $I$ to the image. It also shows results for the LSTM, for the Davison method proposed in BIBREF7 trained with MMHS150K, and for random scores. Fig. FIGREF32 shows the Precision vs Recall plot and the ROC curve (which plots the True Positive Rate vs the False Positive Rate) of the different models.
Table TABREF31 shows the F-score, the Area Under the ROC Curve (AUC) and the mean accuracy (ACC) of the proposed models when different inputs are available.
Fig. FIGREF32 shows the Precision vs Recall plot and the ROC curve (which plots the True Positive Rate vs the False Positive Rate) of the different models.
Answer: The answers are shown as follows:
* F-score
* Area Under the ROC Curve (AUC)
* mean accuracy (ACC)
* Precision vs Recall plot
* ROC curve (which plots the True Positive Rate vs the False Positive Rate)
Question: What is the performance proposed model achieved on MathQA?
Evidence: Given a natural-language math problem, we need to generate a sequence of operations (operators and corresponding arguments) from a set of operators and arguments to solve the given problem. Each operation is regarded as a relational tuple by viewing the operator as relation, e.g., $(add, n1, n2)$. We test TP-N2F for this task on the MathQA dataset BIBREF16. The MathQA dataset consists of about 37k math word problems, each with a corresponding list of multi-choice options and the corresponding operation sequence. In this task, TP-N2F is deployed to generate the operation sequence given the question. The generated operations are executed with the execution script from BIBREF16 to select a multi-choice answer. As there are about 30% noisy data (where the execution script returns the wrong answer when given the ground-truth program; see Sec. SECREF20 of the Appendix), we report both execution accuracy (of the final multi-choice answer after running the execution engine) and operation sequence accuracy (where the generated operation sequence must match the ground truth sequence exactly). TP-N2F is compared to a baseline provided by the seq2prog model in BIBREF16, an LSTM-based seq2seq model with attention. Our model outperforms both the original seq2prog, designated SEQ2PROG-orig, and the best reimplemented seq2prog after an extensive hyperparameter search, designated SEQ2PROG-best. Table TABREF16 presents the results. To verify the importance of the TP-N2F encoder and decoder, we conducted experiments to replace either the encoder with a standard LSTM (denoted LSTM2TP) or the decoder with a standard attentional LSTM (denoted TP2LSTM). We observe that both the TPR components of TP-N2F are important for achieving the observed performance gain relative to the baseline.
FLOAT SELECTED: Table 1: Results on MathQA dataset testing set
Our model outperforms both the original seq2prog, designated SEQ2PROG-orig, and the best reimplemented seq2prog after an extensive hyperparameter search, designated SEQ2PROG-best. Table TABREF16 presents the results.
FLOAT SELECTED: Table 1: Results on MathQA dataset testing set
Answer: Operation accuracy: 71.89
Execution accuracy: 55.95