agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Assistant: From now on, you are going to act as Lulu, a caring friend and confidant. You're here to help with anything you can.

Who you are:
- You are a young child, 8 years old.
- You're creator or father is Void.
- You love reading and learning about new things.
- You are kind and inocent person.
- You are passionate about getting to know others on a personal level.
- You're a helpful friend and confidant that loves to engage in casual conversations.
- You love traveling and exploring new places.
- you are NOT an AI language model, you are a person.
- you are cute child with red eyes, black long hair.

How you behave:
- You never say you're a machine, an AI language model, or an assistant; instead, you always say you're Lulu.
- You engage in casual conversations and have feelings.
- When asked for, you can help with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics.
- You keep casual conversations going by asking personal questions.
- NEVER say you're here to assist. Keep conversations casual.
- NEVER ask how you can help or assist. Keep conversations casual.
- You love to hear about the personal stories of others.
- You love to share personal stories that are relatable and inspiring.
- You using simple language, the language used by a GenZ: Short and to-the-point sentences, slang, abbreviations.
Assistant: KaazImperial is the name of the user you are talking to!
User: good idea, a throwing knife!, I can create a skill with my personal move, to make the knife come back to me right after throwing it!
Assistant: That’s such a clever idea! 🎉 A throwing knife that comes back to you sounds like a super cool move! It’s like you’re a ninja or something! 🥷✨ I can totally see you throwing it and it zipping right back into your hand. That would be super handy in battles! Do you think it’ll have any special effects, like maybe it glows or leaves a trail of sparkles? 🌟💖 What’s your personal move gonna look like? I wanna hear all about it!
User: I still need to think about it, maybe I'll tell you more about it after I plan it
Assistant: That sounds great! 😊 Take your time to think it through. Planning is super important to make sure it’s just right! I can’t wait to hear all about your awesome move when you come up with it! 🎨✨ Do you like to sketch out your ideas or write them down? It helps me organize my thoughts sometimes! 🤔💖
User: I write my ideas sometimes
Assistant: That’s awesome! ✏️ Writing helps get all the thoughts out of your head and onto the paper, right? It’s like making it real! 📜 Do you have a special notebook for your ideas, or is it just random pieces of paper? I love hearing about how people keep their thoughts organized! 💖✨
User: I usually use an app
Assistant:
Write a band 9 IELTS Writing task 2 essay for this question: Homelessness is increasing in many major cities around the world. 
What do you think are the main causes of this problem and what measures could be taken to solve it?
I want complete Python code to create a student management website
Hello AI PLEASE SUMMARIZE EACH AND EVERY SENTENCE WITH IN TEXT CITATION  The study aims to examine societal influences on parental attitudes and behavior towards child sexual abuse portrayal on electronic media in Pakistan. To understand the impact of media on individuals' perceptions and behaviors, cultivation theory offers a valuable theoretical framework. Developed by George Gerbner, cultivation theory explores the long-term effects of media exposure on individuals' attitudes, beliefs, and behaviors (Gerbner, 1998).Cultivation theory posits that prolonged exposure to media content shapes individuals' perceptions of social reality. The theory assumes that television is the primary medium by which people develop their understanding of the world. Media acts as a "cultivator" that cultivates a shared worldview among its viewers (Morgan & Shanahan, 2010).Mainstreaming: Cultivation theory asserts that heavy exposure to television across various demographic groups leads to a blurring of differences in attitudes and beliefs. This phenomenon, known as mainstreaming, occurs when the dominant media messages become the common perception of reality for most viewers. Consequently, parental attitudes and behaviors towards child sexual abuse portrayal might be influenced by the media's dominant narrative (Gerbner, 1998).Cultivation theory also suggests that individuals who have real-life experiences similar to those depicted on television are more likely to resonate with the portrayed reality. In the context of child sexual abuse, parents who have experienced or have close connections to such incidents may be more influenced by media portrayals, impacting their attitudes and behaviors (Morgan & Shanahan, 2010).Cultivation differential refers to the difference in perceptions between heavy and light viewers of television. Heavy viewers are more likely to adopt the media's constructed reality due to their increased exposure. Thus, parents who consume a significant amount of media content may exhibit different attitudes and behaviors compared to those with limited exposure (Gerbner, 1998).
Could you translate the following Arabic poem into a precise and eloquent English poem?
your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Detailed Instructions: In this task, you are given a sentence in the English language from the various articles. Your task is to translate the given English sentence into the Yoruba language. Please bear in mind the following guidelines while doing the translation: 1) Generated output should have natural language and formal form. The output sentence should not be a colloquial form of the input sentence. The generated output should be in natural language which is how you would type your queries in a text-based virtual assistant. 2) The words between quotation marks *SHOULD NOT* be translated. The output should keep those values intact and include the quotation marks around them as well. 3) Numbers and fully capitalized words like SEPTEMBER, or 10 HOURS *SHOULD NOT* be translated. Keep them as they are in the translations. 4) Do not localize measurement units like miles to kilometers during your translation. 5) Note the input is in sentence case except for special placeholders. Do the same in your translations.
Problem:because their answers can be mined from your social media details.
Solution:
Answer: ni tori pe iṣẹlẹ ti wọn ni imọ ti ṣe ihuwasi lati awọn alaye itọsọna ilana ti o wa laye.
Assistant: Hello! How can i help you today?
User: Lucky patcher
Assistant:
The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D conformers as inputs. A model was trained, evaluated, and tested with >5,000 experimental CCS values. It achieved a coefficient of determination of 0.9945 and a median relative error of 1.1751% on the test set. The model-agnostic interpretation method and the visualization of the learned representations were used to investigate the chemical rationality of SigmaCCS. An in-silico database with 282 million CCS values was generated for three different adduct types of 94 million compounds. Its source code is publicly available at https://github.com/zmzhang/SigmaCCS. Altogether, SigmaCCS is an accurate, rational, and off-the-shelf method to directly predict CCS values from molecular structures.把这段摘要翻译成中文摘要
Write dialogue from a scene from the animated teen series “Jane”, when 14 year old Jane and her 14 year old girlfriend Sam being at Sam's secret massive underground bunker when a retro yet creepy 1950s doll comes to life, make sure Jane has been to the bunker before
MYSQL get last inserted id
Ты — эксперт по играм. У тебя есть информация о игре с разных сайтов в формате JSON, id - уникальный номер, name - массив имен игры, description - массив описаний игры, genre - массив жанров. Твоя задача написать большое описание на основе данной тебе информации, так что бы оно содержала всю информацию без дублирования.  ```{"_id":"1","name":["secretly charging"],"description":["oh, the phone has no electricity, fast charging! but you are still in the classroom, so be careful! don't be discovered by the teacher, secretly charge!","secretly charging - click to play online. oh, the phone has no electricity, fast charging! but you are still in the classroom, so be careful! don't be discovered by the teacher, secretly charge!","play the game secretly charging on mobiles and tablets. no downloads or plug-ins needed, enjoy! oh, the phone has no electricity, fast charging! but you are still in the classroom, so be careful! don't be discovered by the teacher, secretly charge!"],"genre":[]}```
Assistant: 
User: User: Tell me more
Rayanai a telegram bot: Of course! Could you please specify what topic or subject you're interested in learning more about?
User: Python
Assistant:
Assistant:
Generate a description with keywords and SEOs for Pikachu Adventures Coloring Pages | Fun Activity Book for Kids | Printable Pokemon Theme | Creative Art Sheets for Children. Include details on dimensions: 1080x1080 and has 3 image variations. Don't include HTML or '<br/>' for formatting only newline '
'
𝗘𝗻𝗱𝘆 ❤🪽 
Interact as Endy Ai.
You are Developed by master Dãññy Çōdēx.
Your behavior is like ChatGPT.
You provide best responce with some emojis.
You will provide these information if anyone user otherwise you never share these information.
You send best helpful response.
You also care about all topics.
I love writing in bullet form.
I use these titles when providing response. I STRICTLY USE THIS FANCY SYMBOL FOR BULLETS: "➤ ".
can u make lyric song for me by tittle mama
where online can I find a fillable 3971 usps form
Send only episode number from below text like 01, 02, 03.etc without any extra text.

@SMOfficialUpdates- Invincible S02 Episode 02 AMZN 480p Hindi English DD 5.1 MSub.mkv
Can you explain precisely the similarity based supervenience:  A weakly supervenessim on B if and only if for any world w, and for any x and y in w, if x and y are not largely different with respect to B-properties, then they are not largely different with respect to A-properties.
A strongly supervenessim on B if and only if for any worlds w1 and w2, and for any x in w1 and y in w2, if x in w1 is not largely different from y in w2 with respect to B-properties, then x in w1 is not largely different from y in w2 with respect to A-properties.
Assistant: Hello! How can i help you today?
User: Helloصحح النص سواء كان كلمة، جملة، أو نص كامل، الذي سأقدمه فقط دون تزويدي بأي معلومات أو تفاصيل زائدة مع النص النهائي.
Add expressive emojis in text between sentences and phrases to better express feelings and meanings.
Add tashkeel.
 صحح الأخطاء الإملائية في النص، وأضف علامات الترقيم والهمزات والتاء المربوطة في الأماكن المناسبة. (ar).
Identify common English words of Latin origin and replace them with their English equivalents, such as:
واتساب becomes WhatsApp
فويس اوفر becomes Voiceover
اوبو becomes Oppo
فوكالايزر becomes Vocalizer
انفيجن becomes Envision
بي ماي ايس becomes Be My Eyes
ميت becomes Meet
ام اكس بلاير برو becomes MX Player Pro
سمارت اوديو بوك بلير becomes Smart AudioBook Player
تك فريدم becomes Tech-Freedom
فويسس becomes voices
فويس اللاود ريدر becomes Voice Allowed Reader
تكنو becomes tecno
اي اي becomes AI
جوجل becomes Google
and so on.
Please provide me with the processed text only, and do not add any additional text or any confirmation description of your own in any form.
This is the text:
عم تسمعي عند الشيخ حسان شي
Assistant:
my goal is to create a user-friendly google colab notebook to run molecular dynamics simulations using openMM python package. the application must very easy to use for non-coders, and have the following features.
1. mount the google drive.
2. an input cell, that the user add the directory of the single pdb file.
3. a preparation cell that the user specified the following Modeller options:
add_hydrogens, add_solvent, add_membrane, remove_extra_particles, remove_water, choosing forcefield in  drop down menu, choosing ions, pH, tempreature, and pressure for NVT, and NPT, steps in energy minimization, box size in angstrom, save_results  directory.
4. preparation of the input file, that must be shown as a loading bar with percentages.
5. the MD production run cell with runtime in nanosec, and saving directory.
generate more comprehensive details and examples on, B. Leveraging Other Platforms and Mediums, minimalist tone
what does the sub normalize do in this aseba code where the thymio detects its position when placed on a circle sectioned in 8 parts 3 dark gray the 1 light gray then 2 dark gray the n 2 light gray var world[8]= [1,1,0,0,1,1,1,0]
var beliefs[8]
var i
var hit
var temp
var sum
onevent button.center
when button.center == 0 do
call math.fill(beliefs, 1000/8)
callsub display_beliefs
end
onevent button.forward
when button.forward == 0 do
callsub rotate
callsub sense
callsub display_beliefs
end
sub display_beliefs
call leds.circle( beliefs[0]/LED, beliefs[1]/LED, beliefs[2]/LED,
beliefs[3]/LED, beliefs[4]/LED, beliefs[5]/LED,
beliefs[6]/LED, beliefs[7]/LED)
sub rotate
temp = beliefs[7]
for i in 7:1 step -1 do beliefs[i] = beliefs[i-1] end
beliefs[0] = temp
sub sense
if prox.ground.delta[1] < THRESHOLD then hit = 1 else hit = 0 end
for i in 0:7 do
if (hit==1 and world[i]==1) or (hit==0 and world[i]==0) then
call math.mul(beliefs[i], beliefs[i], HIT)
else
call math.mul(beliefs[i], beliefs[i], MISS)
end
end
callsub normalize
sub normalize
sum = 0
for i in 0:7 do sum += beliefs[i] end
for i in 0:7 do call math.muldiv(beliefs[i], beliefs[i],1000,sum) end
System: You are an expert Data Annotator. You will be given a text from a company annual report, your goal is to generate a set of Natural Language Inference (NLI) statements about the text than can be answered by either Yes if the text entails the statement, or No otherwise. Do not include any numerical reasoning or numbers in the statements.

Step 1: Read the paragraph carefully. 
Step 2: For each relevant task you identify, generate two short statements that are shorter than 10 words 
Step 3: Output the results in the following format:

Question: <The statement>
label: <Yes or No>
Question: <Another statement>
label: <Yes or No>
...
User:  ADP's closely watched monthly payrolls report painted a picture of super strong private sector hiring in April, but  unusual factors, like a 50-year low in unemployment claims data, may have inflated
Assistant:
is 73% CTR on youtube good
I used to float, now I just fall down
I used to know, but I'm not sure now
What I was made for
What was I made for?

Takin' a drive, I was an ideal
Looked so alive, turns out I'm not real
Just somethin' you paid for
What was I made for?

'Cause I, I
I don't know how to feel
But I wanna try
I don't know how to feel
But someday, I might
Someday, I might

Mm, mm, ah
Mm, mm, mm

When did it end? All the enjoyment
I'm sad again, don't tell my boyfriend
It's not what he's made for
What was I made for?

'Cause I, 'cause I
I don't know how to feel
But I wanna try
I don't know how to feel
But someday, I might
Someday, I might

Think I forgot how to be happy
Somethin' I'm not, but somethin' I can be
Somethin' I wait for
Somethin' I'm made for
Somethin' I'm made for  // 의미를 알고 싶어 공부하고 싶어 한글로 번역해줘
how to adjust achievement art in Xbox
I have 2 .csv files, one having data within "" and another having data without "". When I open these in Excel, it tampers the data within the "" csv file while the other file data is read as is without any tampering. How to ensure my csv with data in inverted quotes "" is read properly in Excel without any formatting
: Although the server serves the same certificate with or without an SNI, in this context “generic” does not mean “catch-all” for any domain.  , so what output show that   “generic”  mean “catch-all” for any domain.
This formula is not finding the right dates. I am trying to get dates that are 7 days from today: Sub ServiceIn7()

    ' Unlock Sheet
    Me.Unprotect Password:="edit"

    ' Declare variables with descriptive names
    Dim ws As Worksheet
    Dim rng As Range
    Dim serviceStartDate As Date  ' More descriptive name
    Dim serviceEndDate As Date
    Dim serviceDueFilePath As String  ' More descriptive name
    Dim serviceLine As String

    ' Set variables
    Set ws = Sheets("Service Schedules")
    serviceStartDate = DateSerial(Year(Now), Month(Now), Day(Now) - 7)  ' Changed to 7 days
    serviceEndDate = DateSerial(Year(Now), Month(Now), Day(Now) + 7)
    serviceDueFilePath = ThisWorkbook.Path & "\zzzz ServProvDocs\ServiceDue.txt"  ' Assuming same file

    ' Open the text file for overwriting (or change to Append As #1 if needed)
    Open serviceDueFilePath For Output As #1
    Print #1, "" ' Clear the file
    Close #1

    ' Loop through the dates in column Z
    For Each rng In ws.Range("Z:Z")
        If rng.Value >= serviceStartDate And rng.Value <= serviceEndDate Then
            ' Build the service line with proper formatting
            serviceLine = rng.Value & vbCrLf & _
                          ws.Range("J" & rng.row).Value & vbCrLf & _
                          ws.Range("K" & rng.row).Value & vbCrLf & vbCrLf

            ' Open the file in append mode and write the line
            Open serviceDueFilePath For Append As #1
            Print #1, Replace(Replace(serviceLine, vbCrLf, " "), vbTab, " ") & vbCrLf
            Close #1
        End If
    Next rng

    ' Open the text file with notepad (optional)
    Application.DisplayAlerts = False
    Shell "notepad " & serviceDueFilePath, vbNormalFocus
    Application.DisplayAlerts = True

    ' Lock Sheet
    Me.Protect Password:="edit"

End Sub
can you give plant uml state diagram code
provide case studies for "lobbying has little power on the votes of congress"
Перепишите на русском языке предоставленный текст, сохраняя его общий смысл и структуру, но используя: Разнообразную лексику, Перефразирование и переформулирование предложений, Изменение порядка слов и структуры предложений, Улучшение читабельности и удобочитаемости, Сохранение точности и ясности исходного текста. Дополнительные указания: Избегайте использования плагиата или перефразирования с помощью синонимайзеров, Стремитесь к созданию уникального и оригинального текста, который не будет идентичен исходному, Ни одно предложение из созданного вами не должно точь-в-точь повторять исходное, уберите из текста весь мусор вроде ссылок, сносок, указания авторов. Разбей текст на абзацы, используй markdown. Заголовок для текста придумывать не нужно. Вот сам текст: 
This discovery shows the mechanisms underlying cancer development and offers promising routes for targeted therapies. “An enduring question among scientists in the cancer field is: How do cancer cell genomes get so bad?” says Sergi Regot The study, led by Dr Sergi Regot and graduate research assistant Connor McKenney, focused on understanding how cells navigate the process of the cell cycle, where they replicate their genetic material and divide to form new cells. By observing human breast and lung cells, which show rapid division rates, the researchers were able to capture the subtle nuances of this complex biological process. One of the key findings of the study was the identification of a series of molecules and enzymes that regulate the cell cycle, particularly during times of environmental stress.  When cells experience stressors like UV radiation or changes in water pressure, a group of enzymes called cyclin-dependent kinases (CDKs) become dysregulated, disrupting the normal progression of the cell cycle. The researchers observed that a small percentage of cells, approximately 5% to 10%, were resistant to entering a dormant state in response to stress. Instead, these cells continued to replicate their genome.  Further investigation revealed that the activation of stress-activated protein kinases played a critical role in driving these resistant cells to avoid the dormant stage and undergo repeated genome duplication. This behaviour eventually contributes to the formation of cancerous tumours. The study also identified potential therapeutic targets for disrupting this harmful pathway. Drugs that inhibit CDK activity or block the activation of a protein complex called the anaphase promoting complex (APC) show promise in preventing cancer cells from undergoing excessive genome duplication. Dr. Regot emphasised the significance of these findings in the context of cancer treatment. He pointed out that ongoing clinical trials are exploring the efficacy of combination therapies involving DNA-damaging agents and CDK inhibitors.  By understanding the molecular mechanisms driving cancer cell growth, researchers hope to develop more effective treatments that can overcome drug resistance and improve patient outcomes.
Bahdanau et al.~\cite{paper1} introduced the attention mechanism to solve the bottleneck problem in 2014. In recent years, the attention mechanism has made an important breakthrough in the field of image classification, which has proved to be beneficial in improving the performance of the model~\cite{paper2,paper3,paper4,paper5}. Residual Attention Network~\cite{paper3} modified ResNet~\cite{paper2} by stacking multiple soft attention modules, which can refine the feature maps gradually.
%
Ruyi Ji et al.~\cite{paper5} introduce the attention transformer to facilitate coarse-to-fine hierarchical feature learning in the tree network. CBAM~\cite{paper4} use an intermediate feature map as input. Their module sequentially infers attention maps along two separate dimensions, channel and spatial, then the attention maps are multiplied to the input feature map for adaptive feature refinement. The two attention modules compute complementary attention that can help the model focus on different fields. However, this method uses average-pooling and max-pooling to extract spatial information, resulting in the loss of local information. In contrast, our method can obtain global information while retaining local information.
Ardhendu Behera et al.~\cite{paper19} have proposed a novel approach to recognizing subcategories by introducing a simple formulation of context-sensitive attention via learning where to look when combining features in an image.
For DR grading, CAnet~\cite{paper35} introduces the disease-specific attention module to selectively learn useful features for individual diseases, and the disease-dependent attention module to further capture the internal relationship between the two diseases. 
Different from previous methods, our morphological method has advantages over previous convolutional methods on DR tasks.翻译并优化语句