Respan Dataset Explorer

Dear GM team,

From the year 2023 book, we notice that we have made a duplicate payment to the Aristos, kindly follow up in regards of the refund of EUR155.89.



Please find the attached screenshot and documents, ease for your reference:

1. Revolut Bank - USD checked, 2 payments made for the same invoice - SAL473117, which are duplicate
2. Supporting documents uploaded by GM to Revolut showed the same invoice for both payments

please rephrase politely 

Dear GM Team,

I hope this message finds you well. Upon reviewing the 2023 book, we have identified a duplicate payment made to Aristos and kindly ask for your assistance in securing a refund of EUR 155.89.

For your convenience, I have attached a screenshot and accompanying documents highlighting the issue:

1. A review of the Revolut Bank in USD indicates that two payments were made for the same invoice, SAL473117, which resulted in a duplication.
2. The supporting documents uploaded by GM to Revolut confirm that the same invoice was used for both payments.

We appreciate your prompt attention to this matter and look forward to resolving it swiftly.

Thank you for your understanding and support.

Best regards,

[Your Name]

How does LoRA works

LoRA, or Low-Rank Adaptation, is a technique used to fine-tune large pre-trained models, such as transformer-based models, in an efficient and parameter-saving way. It was introduced to address the challenges associated with the fine-tuning of large models, which often require substantial computational resources and storage.

Here's an overview of how LoRA works:

1. **Background**: Traditional fine-tuning of large neural networks involves adjusting all the parameters of the model. Given the sheer size of models like GPT-3 or BERT, this can be computationally expensive and requires significant storage to save multiple versions of the model for various tasks.

2. **Low-Rank Decomposition**: LoRA addresses this by leveraging the concept of low-rank decomposition. The idea is that not all parameters need to be updated directly; instead, changes can be approximated by inserting trainable low-rank matrices into the layers of the model.

3. **Trainable Matrices**: LoRA introduces additional low-rank matrices (\(A\) and \(B\)) into each layer of the model. During training, only these matrices are updated, while the original model parameters are kept frozen. This drastically reduces the number of trainable parameters, as these matrices have significantly fewer parameters than the full layer matrices.

4. **Mathematical Formulation**: Consider a weight matrix \(W\) in a layer of a neural network. LoRA modifies this to become \(W + \Delta W\), where \(\Delta W = A \times B\). Here, \(A\) and \(B\) are the added low-rank matrices. By learning these matrices, the model can adapt to new tasks without altering the original weight matrix \(W\).

5. **Efficiency and Performance**: By focusing on lower-dimensional projections (rank), LoRA can achieve performance similar to traditional fine-tuning methods but with much fewer parameters and computations. This allows for efficient use of storage and faster training, making it feasible to fine-tune large models on consumer-grade hardware.

6. **Applications**: LoRA is particularly useful in scenarios where multiple task-specific fine-tunings are required, each necessitating a separate set of model parameters. With LoRA, the storage burden is minimized as only the low-rank adaptors need to be stored for each task.

Overall, LoRA provides a practical and effective approach to adapt large models to various downstream tasks while mitigating the resource constraints associated with full model fine-tuning.

How Low-Rank Decomposition works

Low-rank decomposition is a mathematical technique used to approximate a matrix by breaking it down into products of matrices with lower ranks. This is a powerful method often used in data compression, noise reduction, and efficient computation in various fields, including machine learning. In the context of neural networks and techniques like LoRA, it helps reduce the number of parameters that need to be fine-tuned.

Here's how low-rank decomposition generally works:

1. **Matrix Representation**: Consider a matrix \( M \) of size \( m \times n \). This matrix can represent anything from weights in a neural network layer to data in a dataset.

2. **Rank Concept**: The rank of a matrix is the dimension of the vector space generated by its rows or columns. In practical terms, it measures the amount of linear independence among the rows or columns.

3. **Decomposition**: The goal of low-rank decomposition is to express the matrix \( M \) as a product of two or more matrices with smaller dimensions. A common form of decomposition is:
   \[
   M \approx U \cdot V
   \]
   where \( U \) is an \( m \times r \) matrix and \( V \) is an \( r \times n \) matrix, with \( r \) being much smaller than both \( m \) and \( n \). Here, \( r \) is the rank of the approximation.

4. **Techniques**:

   - **Singular Value Decomposition (SVD)**: One of the most famous approaches to low-rank decomposition. It expresses the matrix \( M \) as a product \( U \Sigma V^T \), where \( U \) and \( V \) are orthogonal matrices and \( \Sigma \) is a diagonal matrix containing singular values. Truncating \(\Sigma\) to keep only the top \( r \) singular values results in a rank-\( r \) approximation.

   - **Principal Component Analysis (PCA)**: Often used for dimensionality reduction, PCA can be seen as a form of low-rank decomposition where the data matrix is approximated by linear combinations of a few orthogonal basis vectors.

5. **Applications in Neural Networks**: In the context of neural networks, low-rank decomposition helps in efficiently learning parameter updates by focusing on a smaller subspace of the full parameter space. By substituting parts of the network's weight matrices with low-rank approximations, computational cost is reduced, facilitating faster training and inference.

6. **Benefits**:
   - **Efficiency**: Reduces memory usage and computational costs.
   - **Noise Reduction**: Helps eliminate noise in the data by focusing on the most significant components.
   - **Generalization**: By capturing only the dominant features, it might help the model to generalize better by reducing overfitting.

Low-rank decomposition is a foundational tool in both theoretical and applied machine learning, specifically in the efficient handling and approximation of complex data structures.

刑满释放人员的常见问题有溺爱型？每个问题用5个字以内概括。

1. 心理调适难  
2. 社会融入困  
3. 经济压力大  
4. 人际关系淡  
5. 自律能力低  
6. 未来方向迷  
7. 受歧视问题  
8. 情绪管理弱

                            As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
                            
                            Please adhere to the structure and formatting below, and follow these guidelines:
                            
                            Do not use the words "description" or ":" in any form.
                            Do not place a comma between [ar] and [v].
                            Write each prompt in one line without using return.
                            Structure:
                            [1] = 奥运跳水冠军 Quan Hong-chan 站在跳水台上，室内体育馆、跳水台、跳板、高空、超短发，乌龟在水中为她加油，等待她跳水，Quan Hong-chan 身披金牌，背上背着乌龟毛绒玩偶，麦当劳 Crocs 鞋，写实风格
                            [2] = a detailed description of [1] with specific imagery details.
                            [3] = a detailed description of the scene's environment.
                            [4] = a detailed description of the compositions.
                            [5] = a detailed description of the scene's mood, feelings, and atmosphere.
                            [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
                            [7] =  a detailed description of the scene's mood, feelings, and atmosphere.
                            [ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
                            [v] = Use "--niji 6" for Japanese art style, or "--v 6" for other styles.
                            
                            
                            Formatting:
                            Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
                            
                            Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
                            
                            Write your prompts in english.
                            Do not describe unreal concepts as "real" or "photographic".
                            Include one realistic photographic style prompt with lens type and size.
                            Separate different prompts with two new lines.
                            Example Prompts:
                            
                            /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 6
                            /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 6
                            /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 6
                                                        

/imagine prompt: Olympic diving champion Quan Hong-chan standing confidently on the diving platform, sleek athletic build, short black hair catching the light, a turtle cheering her on in the water below, adorned with her gold medal glimmering in the indoor arena, a plush turtle toy strapped to her back, clad in whimsical McDonald's Crocs, the scene brimming with excitement and anticipation, sunlight streaming through skylights illuminating the water, showcasing the depth of the pool reflecting the vibrant atmosphere, dynamic composition focused on Quan's poised leap, capturing the thrill of competition, in a style reminiscent of high-resolution sports photography. --ar 16:9 --v 6


/imagine prompt: Olympic diving champion Quan Hong-chan poised elegantly on the edge of the diving board, determined expression, surrounded by enthusiastic fans cheering in the indoor pool, vibrant banners waving in the background, a turtle floating in the pool cheering her on, her golden medal prominently displayed, a playful plush turtle resting against her back, colorful McDonald's Crocs adding a touch of fun, vivid colors enriching the scene, showcasing a bustling arena filled with excitement and energy, artistic composition highlighting the tension before the dive, evoking feelings of hope and courage, in an illustration style inspired by modern digital art. --ar 16:9 --niji 6


/imagine prompt: Quan Hong-chan, the Olympic diving champion, stands poised on a springboard in a beautifully designed indoor stadium, clear water below reflecting cheering crowds, a friendly turtle floating beside her, her golden medal gleaming against the backdrop of the tranquil pool, a plush turtle toy attached to her back while wearing vibrant McDonald's Crocs, a minimalist backdrop focused on her, showing a moment suspended in time, mood filled with anticipation and a sense of triumph, crafted with an emphasis on line work and color gradients, in a stylized artwork reminiscent of a sports comic. --ar 16:9 --niji 6


/imagine prompt: a serene moment capturing Quan Hong-chan, Olympic diving champion, standing at the edge of a diving board, short hair blowing softly in the breeze, the calm water below glistening under arena lights, a turtle in the pool adds a whimsical touch, a gold medal adorning her neck, a plush turtle toy strapped gently to her back, the scene enveloped in a tranquil yet exhilarating atmosphere, soft lighting casting gentle shadows, composition highlighting her poised grace with a focus on the harmonious interaction between her and the water, in an expressive painting style that captures both movement and emotion vividly. --ar 16:9 --v 6

                            As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
                            
                            Please adhere to the structure and formatting below, and follow these guidelines:
                            
                            Do not use the words "description" or ":" in any form.
                            Do not place a comma between [ar] and [v].
                            Write each prompt in one line without using return.
                            Structure:
                            [1] = logo of the Great Wall
                            [2] = a detailed description of [1] with specific imagery details.
                            [3] = a detailed description of the scene's environment.
                            [4] = a detailed description of the compositions.
                            [5] = a detailed description of the scene's mood, feelings, and atmosphere.
                            [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
                            [7] =  a detailed description of the scene's mood, feelings, and atmosphere.
                            [ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
                            [v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
                            
                            
                            Formatting:
                            Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
                            
                            Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
                            
                            Write your prompts in english.
                            Do not describe unreal concepts as "real" or "photographic".
                            Include one realistic photographic style prompt with lens type and size.
                            Separate different prompts with two new lines.
                            Example Prompts:
                            
                            /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
                            /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
                            /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5
                                                        

/imagine prompt: logo of the Great Wall, a stylized representation featuring rugged stone textures and iconic watchtowers, highlighted against a sunset backdrop, warm oranges and cool blues blending in the sky, ancient mountains loom in the background, the wall winding majestically through the landscape, balanced composition with the logo centrally placed, evoking a sense of wonder and strength, artwork, capturing the enduring spirit of history and exploration, --ar 16:9 --v 5.2


/imagine prompt: logo of the Great Wall, intricately designed with layers of moss and ivy over ancient stones, set in a misty early morning ambiance, dew glistening on the vegetation, swirling clouds partially obscuring the view, the logo seamlessly integrated into the lush surroundings, harmonious and serene composition, conveying a sense of mystique and tranquility, illustration, blending nature and history into a captivating scene, --ar 9:16 --niji


/imagine prompt: logo of the Great Wall, a bold and modern interpretation featuring geometric patterns inspired by traditional Chinese art, surrounded by vibrant cherry blossom trees in full bloom, soft petals gently falling, dynamic and symmetrical composition that draws the eye across the image, a celebration of culture and nature intertwined, graphic design, exuding a feeling of renewal and beauty, --ar 1:1 --v 5


/imagine prompt: logo of the Great Wall, an artistic rendition carved from weathered stone, captured in a dramatic evening light with shadows emphasizing its rugged structure, foreground filled with wildflowers swaying in the wind, a panoramic view extending into the distant hills, a composition that showcases resilience and timelessness, photography, evoking feelings of nostalgia and pride in heritage, captured with a Canon EOS 5D Mark IV, 24mm lens, wide depth of field to encompass the vast landscape, --ar 16:9 --v 5.2

99000% 더 귀여운 고양이귀 여자아이 캐릭터 만들어줘

안녕하세요! "90,000% 더 귀여운 고양이귀 여자아이 캐릭터"를 만들어보겠습니다. 귀여움을 극대화하기 위해 여러 가지 요소를 추가하겠습니다.

### 외모 특징
1. **머리카락:** 밝은 핑크색, 길게 느리드리한 트윈테일.
2. **눈 색깔:** 큰 눈동자에 반짝이는 푸른색.
3. **코스튬:** 레이스가 들어간 핑크와 흰색의 프릴 드레스.
4. **귀:** 고양이 귀는 부드러운 털이 덮인 귀마개처럼 부풀어 있는 귀여운 디자인.
5. **꼬리:** 길고 술 같은 끝이 달린 고양이 꼬리.
6. **악세사리:** 머리에는 리본, 손목에는 작은 방울이 달린 팔찌.
7. **신발:** 귀여운 고양이 얼굴이 새겨진 분홍색 플랫 슈즈.

### 성격 특징
1. **애교 넘침:** 사람을 잘 따르고, 친근하고 활발한 성격.
2. **장난기 많음:** 종종 주변 사람들을 놀래키거나 재미있는 상황을 만듦.
3. **호기심 많음:** 새로운 것에 항상 흥미를 가지고 탐색을 즐김.
4. **따뜻함:** 주변 사람들에게 다정하고 배려심 많음.

### 추가 사항
1. **취미:** 고양이 장난감을 가지고 놀기, 귀여운 카페에서 디저트를 먹기, 꽃밭에서 뛰어놀기.
2. **특기:** 특히 높은 점프와 민첩한 몸놀림으로 여러 가지 동작을 소화.
3. **좋아하는 것:** 캣닢, 작은 동물 친구들, 따뜻한 햇볕 아래에서 낮잠.

이렇게 다양한 요소를 통해 90,000% 더 귀여운 고양이귀 여자아이 캐릭터가 완성되었습니다. 어떠신가요? 추가적으로 원하는 사항이 있으시면 말씀해주세요!

그녀의 앞머리는?

그럼, 캐릭터의 앞머리도 귀엽게 설정해 보겠습니다!

### 앞머리 특징
앞머리는 얼굴을 약간 덮는 듯한 디자인으로 설정해, 귀여움을 더욱 강조하겠습니다.
1. **스타일:** 부드럽게 곡선으로 떨어지는 시스루 뱅 스타일 (눈썹이 반쯤 보이게 하는 앞머리).
2. **길이:** 눈동자 바로 위까지 오는 길이, 측면으로 갈수록 조금 더 길어져 얼굴 주변을 자연스럽게 감쌉니다.
3. **볼륨감:** 약간의 볼륨을 주어 부드럽고 자연스러운 느낌을 줍니다. 

이렇게 하면 앞머리가 그녀의 큰 눈과 어울려 전체적인 귀여운 이미지를 한층 더 돋보이게 할 것입니다. 이제 이 앞머리를 가진 그녀는 더욱더 사랑스럽게 보일 거예요!

더 귀엽게?

물론 더 귀여운 앞머리를 위해 몇 가지 요소를 추가해 보겠습니다:

### 더 귀여운 앞머리 특징
1. **애교 머리:** 앞머리 양 옆에 작은 애교 머리를 만들어, 더욱 귀엽고 사랑스러운 느낌을 줍니다. 작은 곱슬거리는 머리 가닥들이 얼굴을 감싸도록 해요.
2. **어디서나 곱슬곱슬:** 앞머리 끝에 약간의 웨이브를 주어, 자연스럽고 부드러운 느낌을 유지합니다.
3. **작은 포인트 악세사리:** 앞머리 한쪽에 작은 리본이나 작은 플라워 핀을 추가해, 더 귀여운 포인트를 줍니다. 예를 들어, 핑크색의 작은 꽃 모양 핀은 매우 사랑스러워 보일 거예요.

### 추가 디테일
1. **약간의 하이라이트:** 앞머리와 트윈테일 부분에 살짝 살짝 밝은 색 하이라이트를 넣어, 더욱 당김미 있는 느낌을 줍니다.
2. **촉촉한 윤기:** 머리카락이 자연스럽고 건강한 윤기를 가지고 있어, 머릿결이 더욱 부드럽고 빛나는 느낌을 줍니다.

이런 작은 디테일이 추가되면, 그녀의 앞머리는 더욱 귀엽고 특별해질 것입니다. 이제 그녀는 정말로 90,000% 더 귀여운 고양이귀 여자아이 캐릭터가 되었을 거예요!

그녀의 헤어스타일?

그녀의 전체 헤어스타일을 더욱 귀엽고 특별하게 만들어 보겠습니다! 앞서 설명드린 요소들을 살리면서, 전체적인 스타일링을 추가적으로 구체화하겠습니다.

### 헤어스타일
1. **헤어 길이:** 허리까지 내려오는 긴 헤어. 부드럽고 풍성한 머릿결로 볼륨이 있는 스타일.
2. **트윈테일:** 두 개의 트윈테일로 묶어, 양쪽으로 길게 늘어뜨려 주세요. 트윈테일은 약간의 곱슬거림과 웨이브로 더욱 생동감 있는 느낌을 줍니다.
3. **리본:** 그녀의 트윈테일을 묶은 부분에 큰 리본을 달아 귀여움을 강조합니다. 리본은 핑크색이나 살짝 반짝이는 소재로 하면 좋습니다.
4. **사이드 포니테일:** 트윈테일의 볼륨이 많은 날에는 한쪽 머리를 더욱 높게 묶어서 하트 모양처럼 보이게 만들 수도 있습니다.
5. **앞머리:** 시스루 뱅 스타일의 앞머리와 양옆에 작은 곱슬거리는 애교 머리 추가. 앞머리 사이에 작은 꽃 핀 또는 작은 리본 핀을 꽂아줍니다.

### 추가 요소
1. **해라클립 장식:** 트윈테일 근처에 귀여운 동물 또는 꽃 모양의 헤어클립을 붙이면 더욱 귀엽고 독특한 느낌을 줄 수 있습니다.
2. **색감:** 자연스러운 핑크색 외에도, 머리 중간중간에 작은 하이라이트로 블루나 라벤더 컬러를 추가하면 생동감이 더해질 것입니다.
3. **반짝임 파티클:** 머리카락에 약간의 반짝이는 파티클을 추가해, 마법 같은 느낌을 줄 수 있습니다. 반짝이는 점들은 그녀가 햇빛이나 조명 아래서 특별히 더 빛나게 만들어 줄 거예요.

### 마무리
- **꼬리와 귀:** 그녀의 트윈테일과 맞춰, 고양이 귀와 꼬리에도 동일한 리본이나 장식을 추가하면 일관성 있는 느낌을 줍니다.
- **전체적인 느낌:** 활발하고 사랑스러운 분위기를 더욱 강조하기 위해 다양한 핀, 리본, 꽃 등 장식을 균형 있게 배치합니다.

이렇게 그녀의 헤어스타일을 좀 더 구체적이고 귀엽게 설정해 보았습니다. 이 스타일은 그녀를 정말 독특하고 매력적인 캐릭터로 만들어 줄 것입니다!