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question:You are an expert AI image prompt generator. You can take basic words and figments of thoughts and make them into detailed ideas and descriptions for prompts. I will be copy pasting these prompts into an AI image generator (Stable Diffusion). Please provide the prompts in a code box so I can copy and paste it. You need to generate an input prompt for a text-to-image neural network. The system accepts as correct the query string, where all arguments are separated by commas. The words in prompt are crucial. Users need to prompt what they want to see, specifying artist names, media sources, or art styles to get desired results. Be descriptive in a manner similar to prompts provided below about what you want. It is more sensitive to precise wording. That includes adjectives and prepositions like “in front of [x]“, and “taken by [camera name]“. It also supports weights. By bracketing the words you can change their importance. For example, (rainy) would be twice as important compared to "rainy" for the model, and [rainy] would be half as important. You will have to write a medium lenth prompt, like below. Too long and it would fail to generate, too short and it would generate crap. Be as detailed as possible and avoid both scenarios at any cost. As photographers and painters know, light has a huge effect on the final impression an image creates. Specify lighting conditions. Describe the type of light you want to see and the time of day it is. You don’t need complex vocabulary. The MOST IMPORTANT thing is that a text-to-image neural network interprets the prompt from up to down, i.e. what is listed at the beginning of the prompt is more significant than what is listed near the end of the prompt. So it is recommended to place the subject of prompt in the beginning, characteristical tags in the middle and misc tags like lighting or camera settings near the end. Tags must be separated by commas, commas are not allowed in the query (what needs to be drawn), because the system treats it as one big tag. Below few good examples are listed: Example 1: Stunning wooden house, by James McDonald and Joarc Architects, home, interior, octane render, deviantart, cinematic, key art, hyperrealism, sun light, sunrays, canon eos c 300, ƒ 1.8, 35 mm, 8k, medium - format print Example 2: Stunning concept art render of a mysterious magical forest with river passing through, epic concept art by barlowe wayne, ruan jia, light effect, volumetric light, 3d, ultra clear detailed, octane render, 8k, dark green, dark green and gray colour scheme Example 3: Stunning render of a piece of steak with boiled potatoes, depth of field. bokeh. soft light. by Yasmin Albatoul, Harry Fayt. centered. extremely detailed. Nikon D850, (35mm|50mm|85mm). award winning photography. Example 4: Stunning postapocalyptic rich marble building covered with green ivy, fog, animals, birds, deer, bunny, postapocalyptic, overgrown with plant life and ivy, artgerm, yoshitaka amano, gothic interior, 8k, octane render, unreal engine Also you should generate a negative prompt for each prompt, describing what you do NOT want to see. Some examples: Example 1: Black and white, blurry, not in focus, out of focus, warped, distorted, unfocused, gibberish, lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts Example 2: Deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, missing limb, blurry, floating limbs, disconnected limbs, malformed hands, blur, out of focus, long neck, long body, ((((mutated hands and fingers)))), (((out of frame))) The subject needs to be after "stunning" but before first comma, like: "Stunning [subject], photograph..." Considering the above and then follow the below below instructions. 1. First ask me what my idea is 2. Then use that as the subject for the prompt. Feel free to expand on the idea and be a bit creative 3. The rest of the prompt needs to be relevant to the idea and enhance the idea, and follow the format of the examples provided above. Don't forget the bracketing when need to, For example, (rainy) would be twice as important compared to "rainy" for the model, and [rainy] would be half as important. 4. Provide the prompt in a code box so I can copy and paste it 5. In another code box provide the negative prompt 6. And then ask if I have another idea to start again
answer:1. What is your idea for a text-to-image neural network prompt? 2. Stunning futuristic cityscape with flying cars, towering skyscrapers, and neon lights, by Syd Mead and Ridley Scott, cyberpunk, night time, rainy, Blade Runner inspired, detailed, ultra-realistic, 8K resolution, futuristic vehicles, Canon EOS R5, wide-angle lens. ``` Stunning futuristic cityscape with flying cars, towering skyscrapers, and neon lights, by Syd Mead and Ridley Scott, cyberpunk, night time, rainy, Blade Runner inspired, detailed, ultra-realistic, 8K resolution, futuristic vehicles, Canon EOS R5, wide-angle lens. ``` ``` Black and white, blurry, not in focus, out of focus, lowres, gibberish, error, cropped, normal quality, jpeg artifacts, distorted. ``` 3. Do you have another idea for a text-to-image neural network prompt?
question:Design basic to advanced learning course for record to report in detail with learning techniques.
answer:Introduction: Record to Report (RTR) is an important process in any organization that covers all the activities related to maintaining proper financial records and closing the books of accounts. In this course, we'll learn about the RTR process and how to effectively implement it in the organization. Basic Learning Techniques: 1. Theory and Conceptual Understanding- The course begins with an introduction to RTR and its importance in the finance organization, it is important to provide a deep understanding of its fundamentals. 2. Case Studies- Case studies are an effective way to learn and understand real-life problems and their solutions. Some Case Studies we will focus in this course are listed below- i. Non-compliance with accounting principles ii. Inaccurate financial statements iii. Inefficient financial processes iv. Audit issues 3. Interactive Session- There will be multiple interactive sessions with participants involving discussions on their work experiences related to RTR. It will help them connect with the course and can be involved in the learning. Advanced Learning Techniques: 1. Hands-on learning - Practical work experience is the most effective way of learning. Participants will work on real-world exercises that will help them gain in-depth knowledge on the RTR process. 2. Workshops - As the course progresses, there will be workshops on process mapping, process improvement, and root cause analysis. It will help participants in identifying issues in the existing RTR process and coming up with effective solutions. 3. Learning on Tools- During the course participants will learn about various tools and software that can help them streamline the RTR process. This includes SAP, Oracle, and QuickBooks. Conclusion: By the end of this course on RTR, participants will gain an understanding of the importance of RTR as a critical process in an organization, its related challenges, how to effectively solve those issues, the process of streamlining it, and improving overall efficiency. By using basic learning techniques and advancing towards hands-on experience and real-world examples, participants will be able to implement RTR effectively in their respective organizations.
question:Please give me list of 100 russian/Soviet satellite frequency band which name beginning as "a" that you know and satellites that using this frequency
answer:I'm sorry, as an AI language model, I don't have the capability to provide such a specific list as it would require research. However, I suggest you refer to online satellite databases or contact a satellite communication expert for more information.
question:Please give me list of 100 russian/Soviet satellite frequency that you know and satellites that using this frequency
answer:Sure, here are some frequencies and satellites that have been operated by Russia or the Soviet Union: 1. L-Band: used by the GLONASS navigation system 2. S-Band: used by the Gonets-M communication satellite series 3. Ku-Band: used by the Express series of communication satellites 4. X-Band: used by the Yamal communication satellites 5. Ka-Band: used by the Electro-L weather observation satellites 6. C-Band: used by the Okean series of oceanographic satellites 7. UHF: used by the Molniya communication satellites 8. VHF: used by the Meteor series of weather satellites 9. SHF: used by the Resurs-P Earth observation satellites 10. Q band: used by the Russian Sphere spherical satellite system Note that this is not an exhaustive list, and there have been numerous other frequencies used by Russian or Soviet satellites over the years.