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Table of Links Abstract and 1. Introduction 2 Related Work 3 Preliminaries 4 Method 4.1 Key Sample and Joint Editing 4.2 Edit Propagation Via TokenFlow 5 Results 5.1 Qualitative Evaluation and 5.2 Quantitative Evaluation 5.3 Ablation Study 6 Discussion 7 Acknowledgement and References A Implementation Details A IMPLEMENTATION DETAILS StableDiffusion. We use Stable Diffusion as our pre-trained text-to-image model; we use the StableDiffusion-v-2-1 checkpoint provided via official HuggingFace webpage. DDIM inversion. In all of our experiments, we use DDIM deterministic sampling with 50 steps. For inverting the video, we follow Tumanyan et al. (2023) and use DDIM inversion with classifierfree guidance scale of 1 and 1000 forward steps; and extract the self-attention input tokens from this process similarly to Qi et al. (2023). Runtime. Since we don’t compute the attention module on most video frames (i.e., we only compute the self-attention output on the keyframes) our method is efficient in run-time, and the sampling of the video reduces the time of per-frame editing by 20%. The inversion process with 1000 steps is the main bottleneck of our method in terms of run-time, and in many cases a significantly smaller amount of steps is suffieicent (e.g. 50). Table 3 reports runtime comparisons using 50 steps in all methods. Notably, our sampling time is indeed faster than that of per-frame editing (PnP). Baselines. For running the baseline of Tune-a-video (Wu et al., 2022) we used their official repository. For Gen-1 (Esser et al., 2023) we used their platform on Runaway website. This platform outputs a video that is not in the same length and frame-rate as the input video; therefore, we could not compute the warping error on their results. For text-to-video-zero (Khachatryan et al., 2023b) we used their official repository, with their depth conditioning configuration. For Fate-Zero (Qi et al., 2023) with used their official repository, and verified the run configurations with the authors. This paper is available on arxiv under CC BY 4.0 DEED DEED license. Authors:
(1) Michal Geyer, Weizmann Institute of Science and Indicates equal contribution;
(2) Omer Bar-Tal, Weizmann Institute of Science and Indicates equal contribution;
(3) Shai Bagon, Weizmann Institute of Science;
(4) Tali Dekel, Weizmann Institute of Science. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 Preliminaries 3 Preliminaries 4 Method 4 Method 4.1 Key Sample and Joint Editing 4.1 Key Sample and Joint Editing 4.2 Edit Propagation Via TokenFlow 4.2 Edit Propagation Via TokenFlow 5 Results 5 Results 5.1 Qualitative Evaluation and 5.2 Quantitative Evaluation 5.1 Qualitative Evaluation and 5.2 Quantitative Evaluation 5.3 Ablation Study 5.3 Ablation Study 6 Discussion 6 Discussion 7 Acknowledgement and References 7 Acknowledgement and References A Implementation Details A Implementation Details A IMPLEMENTATION DETAILS StableDiffusion. We use Stable Diffusion as our pre-trained text-to-image model; we use the StableDiffusion-v-2-1 checkpoint provided via official HuggingFace webpage. StableDiffusion. DDIM inversion. In all of our experiments, we use DDIM deterministic sampling with 50 steps. For inverting the video, we follow Tumanyan et al. (2023) and use DDIM inversion with classifierfree guidance scale of 1 and 1000 forward steps; and extract the self-attention input tokens from this process similarly to Qi et al. (2023). DDIM inversion. Runtime. Since we don’t compute the attention module on most video frames (i.e., we only compute the self-attention output on the keyframes) our method is efficient in run-time, and the sampling of the video reduces the time of per-frame editing by 20%. The inversion process with 1000 steps is the main bottleneck of our method in terms of run-time, and in many cases a significantly smaller amount of steps is suffieicent (e.g. 50). Table 3 reports runtime comparisons using 50 steps in all methods. Notably, our sampling time is indeed faster than that of per-frame editing (PnP). Runtime. Baselines. For running the baseline of Tune-a-video (Wu et al., 2022) we used their official repository. For Gen-1 (Esser et al., 2023) we used their platform on Runaway website. This platform outputs a video that is not in the same length and frame-rate as the input video; therefore, we could not compute the warping error on their results. For text-to-video-zero (Khachatryan et al., 2023b) we used their official repository, with their depth conditioning configuration. For Fate-Zero (Qi et al., 2023) with used their official repository, and verified the run configurations with the authors. Baselines. This paper is available on arxiv under CC BY 4.0 DEED DEED license. This paper is available on arxiv under CC BY 4.0 DEED DEED license. available on arxiv Authors: (1) Michal Geyer, Weizmann Institute of Science and Indicates equal contribution; (2) Omer Bar-Tal, Weizmann Institute of Science and Indicates equal contribution; (3) Shai Bagon, Weizmann Institute of Science; (4) Tali Dekel, Weizmann Institute of Science. Authors: Authors: (1) Michal Geyer, Weizmann Institute of Science and Indicates equal contribution; (2) Omer Bar-Tal, Weizmann Institute of Science and Indicates equal contribution; (3) Shai Bagon, Weizmann Institute of Science; (4) Tali Dekel, Weizmann Institute of Science.

TokenFlow's Implementation Details: Everything That We Used

Table of Links Abstract and Introduction
Related Work
Problem Definition
Method
Experiments
Conclusion and References A. Appendix A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A.4 Environment Details A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference A.4 ENVIRONMENT DETAILS In Table 4, we present the observation and action dimensions, along with the task description and task metrics for 9 tasks in IsaacGym. Authors:
(1) Chao Yu, Tsinghua University;
(2) Hong Lu, Tsinghua University;
(3) Jiaxuan Gao, Tsinghua University;
(4) Qixin Tan, Tsinghua University;
(5) Xinting Yang, Tsinghua University;
(6) Yu Wang, with equal advising from Tsinghua University;
(7) Yi Wu, with equal advising from Tsinghua University and the Shanghai Qi Zhi Institute;
(8) Eugene Vinitsky, with equal advising from New York University (zoeyuchao@gmail.com). This paper is available on arxiv under CC 4.0 license. Table of Links Abstract and Introduction Related Work Problem Definition Method Experiments Conclusion and References Abstract and Introduction Abstract and Introduction Related Work Related Work Problem Definition Problem Definition Method Method Experiments Experiments Conclusion and References Conclusion and References A. Appendix A. Appendix A.1. Full Prompts and A.2 ICPL Details A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A. 3 Baseline Details A.4 Environment Details A.4 Environment Details A.5 Proxy Human Preference A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference A.6 Human-in-the-Loop Preference A.4 ENVIRONMENT DETAILS In Table 4, we present the observation and action dimensions, along with the task description and task metrics for 9 tasks in IsaacGym. Authors: (1) Chao Yu, Tsinghua University; (2) Hong Lu, Tsinghua University; (3) Jiaxuan Gao, Tsinghua University; (4) Qixin Tan, Tsinghua University; (5) Xinting Yang, Tsinghua University; (6) Yu Wang, with equal advising from Tsinghua University; (7) Yi Wu, with equal advising from Tsinghua University and the Shanghai Qi Zhi Institute; (8) Eugene Vinitsky, with equal advising from New York University (zoeyuchao@gmail.com). Authors: Authors: (1) Chao Yu, Tsinghua University; (2) Hong Lu, Tsinghua University; (3) Jiaxuan Gao, Tsinghua University; (4) Qixin Tan, Tsinghua University; (5) Xinting Yang, Tsinghua University; (6) Yu Wang, with equal advising from Tsinghua University; (7) Yi Wu, with equal advising from Tsinghua University and the Shanghai Qi Zhi Institute; (8) Eugene Vinitsky, with equal advising from New York University ( zoeyuchao@gmail.com ). zoeyuchao@gmail.com This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Few-shot In-Context Preference Learning Using Large Language Models: Environment Details

Table of Links Abstract and Introduction
Related Work
Problem Definition
Method
Experiments
Conclusion and References A. Appendix A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A.4 Environment Details A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference A APPENDIX We would suggest visiting https://sites.google.com/view/few-shot-icpl/home for more information and videos. A.1 FULL PROMPTS A.2 ICPL DETAILS The full pseudocode of ICPL is listed in Algo. 2. Authors:
(1) Chao Yu, Tsinghua University;
(2) Hong Lu, Tsinghua University;
(3) Jiaxuan Gao, Tsinghua University;
(4) Qixin Tan, Tsinghua University;
(5) Xinting Yang, Tsinghua University;
(6) Yu Wang, with equal advising from Tsinghua University;
(7) Yi Wu, with equal advising from Tsinghua University and the Shanghai Qi Zhi Institute;
(8) Eugene Vinitsky, with equal advising from New York University (zoeyuchao@gmail.com). This paper is available on arxiv under CC 4.0 license. Table of Links Abstract and Introduction Related Work Problem Definition Method Experiments Conclusion and References Abstract and Introduction Abstract and Introduction Related Work Related Work Problem Definition Problem Definition Method Method Experiments Experiments Conclusion and References Conclusion and References A. Appendix A. Appendix A.1. Full Prompts and A.2 ICPL Details A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A. 3 Baseline Details A.4 Environment Details A.4 Environment Details A.5 Proxy Human Preference A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference A.6 Human-in-the-Loop Preference A APPENDIX We would suggest visiting https://sites.google.com/view/few-shot-icpl/home for more information and videos. A.1 FULL PROMPTS A.2 ICPL DETAILS The full pseudocode of ICPL is listed in Algo. 2. Authors: (1) Chao Yu, Tsinghua University; (2) Hong Lu, Tsinghua University; (3) Jiaxuan Gao, Tsinghua University; (4) Qixin Tan, Tsinghua University; (5) Xinting Yang, Tsinghua University; (6) Yu Wang, with equal advising from Tsinghua University; (7) Yi Wu, with equal advising from Tsinghua University and the Shanghai Qi Zhi Institute; (8) Eugene Vinitsky, with equal advising from New York University (zoeyuchao@gmail.com). Authors: Authors: (1) Chao Yu, Tsinghua University; (2) Hong Lu, Tsinghua University; (3) Jiaxuan Gao, Tsinghua University; (4) Qixin Tan, Tsinghua University; (5) Xinting Yang, Tsinghua University; (6) Yu Wang, with equal advising from Tsinghua University; (7) Yi Wu, with equal advising from Tsinghua University and the Shanghai Qi Zhi Institute; (8) Eugene Vinitsky, with equal advising from New York University ( zoeyuchao@gmail.com ). zoeyuchao@gmail.com This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Few-shot In-Context Preference Learning Using Large Language Models: Full Prompts and ICPL Details

Table of Links Abstract and 1 Introduction 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 6 Related Works 7 Conclusions and References A Additional Experiments B Additional Qualitative Examples C Implementation Details D Additional Related Works E Limitations, Failure Cases & Future Works C IMPLEMENTATION DETAILS C.1 TRAINING DATA & HYPER-PARAMETERS In Table 11, we list the detailed training dataset usage and hyper-parameters. The training data are constructed based on the following datasets: a) LAION400M (Schuhmann et al., 2021), b) LAIONCOCO (Schuhmann et al., 2023), c) MMC4 (Zhu et al., 2023b), d) BLIP-LAION (Li et al., 2022) which is filtered and caption by BLIP (Li et al., 2022), e) LLaVAPretrain (Liu et al., 2023a) which contains 558K image-text pairs from BLIP-captioned CC3M (Sharma et al., 2018), SBU (Ordonez et al., 2011), and LAION400M filtered by LLaVA, f) LLaVAInstruct (Liu et al., 2023a), which contains 80K visual instruction-following data constructed by LLaVA, and g) InstructMMC4, which is our instruction-following interleaved document generation data curated by prompting GPT-4 to generate instruction based on the text contents of MMC4. h) Instruct-BLIP-LAION, which is our instruction-following image synthesis data. Similar to InstructMMC4, it is curated by prompting GPT-4 to generate instructions based on image captions. Unless otherwise specified, we randomly sample the indicated number of instances from each dataset during the training process. C.2 DREAMLLM MODEL Language Model We use LLaMA-1 (Touvron et al., 2023a) trained on ShareGPT (Zheng et al., 2023) as as the default LLM (i.e., Vicuna-7B[1] (Chiang et al., 2023)) following Liu et al. (2023a) to endow its instruction-following capacity. During training, we use Flash Attention (Dao et al., 2022) and PyTorch FSDP (Zhao et al., 2023b) to accelerate training efficiency. Visual Encoder The visual encoder is the publicly available OpenAI CLIP-L/14 (Radford et al., 2021) model, which is frozen during the whole process. The images are resized to 224×224 resolution to align with the CLIP pretraining settings, resulting in a sequence of 256 total tokens for each image. Following prior VL practice (Lu et al., 2019; Liu et al., 2023a), we append a special  token before the image sequence and a special  at the end of the sequence. Diffusion Image Decoder We adopt SDv2.1 (Rombach et al., 2022) trained on 512×512 resolution as the default diffusion image decoder. Same as the visual encoder, the SD model is frozen without any modifications or training throughout the whole process. When constructing the SD target to compute the MSE loss, we resize the images to 512 resolution to fit its pretraining configuration. Dream Query We use dream queries to gather semantic context from MLLMs as introduced before in Sec. 3. Without specifications, we use 64 learnable query embeddings. It is both efficient and effective in generating high-quality images. In order to predict when to generate images, we also introduce the special token, which is appended before the dream query sequence. A is appended at the end of the sequence, similar to image inputs. C.3 EVALUATION BENCHMARKS Systemic evaluations of DREAMLLM regarding VL comprehension, content creation, and NLP capabilities have been conducted. See the used benchmarks and datasets listed in Table 11. During the evaluation, we use the prompt templates listed in Fig. 12. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. [1] Vicuna-7B v1.1: https://huggingface.co/lmsys/vicuna-7b-v1.1. Authors:
(1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII;
(2) Chunrui Han, MEGVII Technology;
(3) Yuang Peng, Tsinghua University and Internship at MEGVII;
(4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII;
(5) Zheng Ge, MEGVII Technology;
(6) Jinrong Yang, HUST and Internship at MEGVII;
(7) Liang Zhao, MEGVII Technology;
(8) Jianjian Sun, MEGVII Technology;
(9) Hongyu Zhou, MEGVII Technology;
(10) Haoran Wei, MEGVII Technology;
(11) Xiangwen Kong, MEGVII Technology;
(12) Xiangyu Zhang, MEGVII Technology and a Project leader;
(13) Kaisheng Ma, Tsinghua University and a Corresponding author;
(14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background & Problem Statement 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 5. 2 What is learned by DreamLLM? 6 Related Works 6 Related Works 7 Conclusions and References 7 Conclusions and References A Additional Experiments A Additional Experiments B Additional Qualitative Examples B Additional Qualitative Examples C Implementation Details C Implementation Details D Additional Related Works D Additional Related Works E Limitations, Failure Cases & Future Works E Limitations, Failure Cases & Future Works C IMPLEMENTATION DETAILS C.1 TRAINING DATA & HYPER-PARAMETERS In Table 11, we list the detailed training dataset usage and hyper-parameters. The training data are constructed based on the following datasets: a) LAION400M (Schuhmann et al., 2021), b) LAIONCOCO (Schuhmann et al., 2023), c) MMC4 (Zhu et al., 2023b), d) BLIP-LAION (Li et al., 2022) which is filtered and caption by BLIP (Li et al., 2022), e) LLaVAPretrain (Liu et al., 2023a) which contains 558K image-text pairs from BLIP-captioned CC3M (Sharma et al., 2018), SBU (Ordonez et al., 2011), and LAION400M filtered by LLaVA, f) LLaVAInstruct (Liu et al., 2023a), which contains 80K visual instruction-following data constructed by LLaVA, and g) InstructMMC4, which is our instruction-following interleaved document generation data curated by prompting GPT-4 to generate instruction based on the text contents of MMC4. h) Instruct-BLIP-LAION, which is our instruction-following image synthesis data. Similar to InstructMMC4, it is curated by prompting GPT-4 to generate instructions based on image captions. Unless otherwise specified, we randomly sample the indicated number of instances from each dataset during the training process. C.2 DREAMLLM MODEL Language Model We use LLaMA-1 (Touvron et al., 2023a) trained on ShareGPT (Zheng et al., 2023) as as the default LLM (i.e., Vicuna-7B[1] (Chiang et al., 2023)) following Liu et al. (2023a) to endow its instruction-following capacity. During training, we use Flash Attention (Dao et al., 2022) and PyTorch FSDP (Zhao et al., 2023b) to accelerate training efficiency. Visual Encoder The visual encoder is the publicly available OpenAI CLIP-L/14 (Radford et al., 2021) model, which is frozen during the whole process. The images are resized to 224×224 resolution to align with the CLIP pretraining settings, resulting in a sequence of 256 total tokens for each image. Following prior VL practice (Lu et al., 2019; Liu et al., 2023a), we append a special  token before the image sequence and a special  at the end of the sequence. Visual Encoder Diffusion Image Decoder We adopt SDv2.1 (Rombach et al., 2022) trained on 512×512 resolution as the default diffusion image decoder. Same as the visual encoder, the SD model is frozen without any modifications or training throughout the whole process. When constructing the SD target to compute the MSE loss, we resize the images to 512 resolution to fit its pretraining configuration. Diffusion Image Decoder Dream Query We use dream queries to gather semantic context from MLLMs as introduced before in Sec. 3. Without specifications, we use 64 learnable query embeddings. It is both efficient and effective in generating high-quality images. In order to predict when to generate images, we also introduce the special token, which is appended before the dream query sequence. A is appended at the end of the sequence, similar to image inputs. Dream Query C.3 EVALUATION BENCHMARKS Systemic evaluations of DREAMLLM regarding VL comprehension, content creation, and NLP capabilities have been conducted. See the used benchmarks and datasets listed in Table 11. During the evaluation, we use the prompt templates listed in Fig. 12. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv [1] Vicuna-7B v1.1: https://huggingface.co/lmsys/vicuna-7b-v1.1. Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Authors: Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader.

DreamLLM: Crucial Implementation Details

The HackerNoon Newsletter: Lumoz Unveils esMOZ Airdrop Details (11/17/2024)

We're thrilled to announce the forthcoming Lumoz’s esMOZ airdrop, distributed to all sorts of contributors! This milestone wouldn't have been possible without the dedication and contributions of our vibrant community. Over the last two years, our community has supported Lumoz through key phases: the Pre-Alpha testnet engaged 87,567 active participants, the Alpha testnet attracted 286,179 followers, and participation in the Quidditch testnet exceeded one million. These numbers underscore the community’s strong support and trust in Lumoz. Your contributions have driven invaluable feedback, strengthening Lumoz’s technical foundation and shaping our ecosystem's growth. This airdrop is our way of expressing gratitude for your continued support and is a significant step toward our journey into the next chapter. Airdrop Rules Airdrop Inquiry Link: https://lumoz.org/airdrop Lumoz Tokens have a total supply of 10 billion, with 10% (1 billion esMOZ tokens) dedicated to this airdrop. Based on our recent valuation, this represents a value of approximately $30 million. The exchange rate for esMOZ to MOZ will be 1:1, and the exchange will follow an unlocking schedule as detailed in our official documentation (https://docs.lumoz.org/). As the largest airdrop in Lumoz's history, it will target early adopters, Lumoz PoW & node holders, ecosystem contributors, and active participants from ecosystems like ETH, Celestia, and the Move Ecosystem. Detailed Eligibility Criteria: Early Lumoz Participants:  Lumoz Point holders will receive esMOZ on a 1:1 basis for points held. Users with over 500 points will also receive bonus esMOZ airdrops.


Lumoz PoW Testnet Participants: From November 5 to December 5, Lumoz will host ZK mining on the testnet. Participants will earn esMOZ airdrops proportional to MOZ test tokens mined during this period. Updated rewards will be available on the airdrop page.


Lumoz zkVerifier Node Holders:  Holders of the Lumoz zkVerifier License, as captured in the October 26 snapshot, are eligible for airdrops.


Lumoz Ecosystem Users: This includes users of Lumoz RaaS services in projects like Merlin Chain, ZKFair, Ultiverse, and Matr1x. Active users in these ecosystems will receive direct airdrops, visible on the official airdrop page starting November 5. This portion of the airdrop will consist of two parts: The first part will be directly airdropped to active addresses in the ecosystem, which can be checked on the Lumoz official airdrop page starting November 5. The second part will be airdropped to users participating in ecosystem-related activities. After the events conclude, the esMOZ earned through participation will be updated on the Lumoz official airdrop page. Please follow official updates from each ecosystem for specific activity times. Active Users in Ecosystems like ETH, Celestia, and Move: Eligible users include: Participants interacting with major DeFi projects in ETH, including Ethereum, Polygon, and Arbitrum over the past six months.


Significant stakers in the Celestia network. Active users engaging with leading DeFi projects in Move, primarily Aptos and Sui, within the past six months. Please note that the airdrop inquiry period will last one month, during which, specific claim details will be announced. NFT Activity Coming Soon After obtaining esMOZ, users are naturally most concerned about the exchange rules. Lumoz's official documentation has previously stated that the exchange ratio between esMOZ and MOZ is 1:1, although there will be a certain unlocking period. While this setup is designed for Lumoz's long-term development, there have been voices in the community expressing a desire for an earlier unlocking. We value the feedback from every community user, which is why we have introduced an NFT section that will allow users to directly exchange a certain amount of esMOZ for MOZ. NFTs will be set at different levels, with each level offering varying esMOZ exchange limits. In the future, NFTs will also support free circulation, allowing users to exchange according to their needs for flexible asset management. We will announce the rules for obtaining NFTs shortly, so stay tuned! This airdrop marks just the beginning for Lumoz. Follow our official Twitter for updates and stay informed on our journey forward. About Lumoz Lumoz is a leading modular computing layer and ZK-RaaS platform, delivering computation for Rollups, ZK-ML, and ZKP verification through its PoW mining system. Earlier this year, Lumoz completed its Pre-A funding round, raising $14 million with backing from prominent investors like OKX Ventures, HashKey Capital, KuCoin Ventures, Polygon, and IDG Blockchain. With over 1.5 million users, 200+ projects, 28,137 validators, and 145 PoW miners engaged, Lumoz’s mainnet is set to launch by year’s end. We're thrilled to announce the forthcoming Lumoz’s esMOZ airdrop, distributed to all sorts of contributors! This milestone wouldn't have been possible without the dedication and contributions of our vibrant community. Over the last two years, our community has supported Lumoz through key phases: the Pre-Alpha testnet engaged 87,567 active participants, the Alpha testnet attracted 286,179 followers, and participation in the Quidditch testnet exceeded one million. These numbers underscore the community’s strong support and trust in Lumoz. Your contributions have driven invaluable feedback, strengthening Lumoz’s technical foundation and shaping our ecosystem's growth. This airdrop is our way of expressing gratitude for your continued support and is a significant step toward our journey into the next chapter. Airdrop Rules Airdrop Inquiry Link: https://lumoz.org/airdrop https://lumoz.org/airdrop https://lumoz.org/airdrop Lumoz Tokens have a total supply of 10 billion, with 10% (1 billion esMOZ tokens) dedicated to this airdrop. Based on our recent valuation, this represents a value of approximately $30 million. The exchange rate for esMOZ to MOZ will be 1:1, and the exchange will follow an unlocking schedule as detailed in our official documentation ( https://docs.lumoz.org/ ). https://docs.lumoz.org/ As the largest airdrop in Lumoz's history, it will target early adopters, Lumoz PoW & node holders, ecosystem contributors, and active participants from ecosystems like ETH, Celestia, and the Move Ecosystem. Detailed Eligibility Criteria: Detailed Eligibility Criteria: Early Lumoz Participants:  Lumoz Point holders will receive esMOZ on a 1:1 basis for points held. Users with over 500 points will also receive bonus esMOZ airdrops. Lumoz PoW Testnet Participants: From November 5 to December 5, Lumoz will host ZK mining on the testnet. Participants will earn esMOZ airdrops proportional to MOZ test tokens mined during this period. Updated rewards will be available on the airdrop page. Lumoz zkVerifier Node Holders:  Holders of the Lumoz zkVerifier License, as captured in the October 26 snapshot, are eligible for airdrops. Lumoz Ecosystem Users: This includes users of Lumoz RaaS services in projects like Merlin Chain, ZKFair, Ultiverse, and Matr1x. Active users in these ecosystems will receive direct airdrops, visible on the official airdrop page starting November 5. This portion of the airdrop will consist of two parts: Early Lumoz Participants:  Lumoz Point holders will receive esMOZ on a 1:1 basis for points held. Users with over 500 points will also receive bonus esMOZ airdrops. Early Lumoz Participants:  Lumoz Point holders will receive esMOZ on a 1:1 basis for points held. Users with over 500 points will also receive bonus esMOZ airdrops. Lumoz PoW Testnet Participants: From November 5 to December 5, Lumoz will host ZK mining on the testnet. Participants will earn esMOZ airdrops proportional to MOZ test tokens mined during this period. Updated rewards will be available on the airdrop page. Lumoz PoW Testnet Participants: From November 5 to December 5, Lumoz will host ZK mining on the testnet. Participants will earn esMOZ airdrops proportional to MOZ test tokens mined during this period. Updated rewards will be available on the airdrop page. Lumoz zkVerifier Node Holders:  Holders of the Lumoz zkVerifier License, as captured in the October 26 snapshot, are eligible for airdrops. Lumoz zkVerifier Node Holders:  Holders of the Lumoz zkVerifier License, as captured in the October 26 snapshot, are eligible for airdrops. Lumoz Ecosystem Users: This includes users of Lumoz RaaS services in projects like Merlin Chain, ZKFair, Ultiverse, and Matr1x. Active users in these ecosystems will receive direct airdrops, visible on the official airdrop page starting November 5. This portion of the airdrop will consist of two parts: Lumoz Ecosystem Users: This includes users of Lumoz RaaS services in projects like Merlin Chain, ZKFair, Ultiverse, and Matr1x. Active users in these ecosystems will receive direct airdrops, visible on the official airdrop page starting November 5. This portion of the airdrop will consist of two parts: The first part will be directly airdropped to active addresses in the ecosystem, which can be checked on the Lumoz official airdrop page starting November 5. The first part will be directly airdropped to active addresses in the ecosystem, which can be checked on the Lumoz official airdrop page starting November 5. The second part will be airdropped to users participating in ecosystem-related activities. After the events conclude, the esMOZ earned through participation will be updated on the Lumoz official airdrop page. Please follow official updates from each ecosystem for specific activity times. The second part will be airdropped to users participating in ecosystem-related activities. After the events conclude, the esMOZ earned through participation will be updated on the Lumoz official airdrop page. Please follow official updates from each ecosystem for specific activity times. Active Users in Ecosystems like ETH, Celestia, and Move: Eligible users include: Active Users in Ecosystems like ETH, Celestia, and Move: Eligible users include: Participants interacting with major DeFi projects in ETH, including Ethereum, Polygon, and Arbitrum over the past six months. Significant stakers in the Celestia network. Participants interacting with major DeFi projects in ETH, including Ethereum, Polygon, and Arbitrum over the past six months. Participants interacting with major DeFi projects in ETH, including Ethereum, Polygon, and Arbitrum over the past six months. Significant stakers in the Celestia network. Significant stakers in the Celestia network. Active users engaging with leading DeFi projects in Move, primarily Aptos and Sui, within the past six months. Please note that the airdrop inquiry period will last one month, during which, specific claim details will be announced. Active users engaging with leading DeFi projects in Move, primarily Aptos and Sui, within the past six months. Please note that the airdrop inquiry period will last one month, during which, specific claim details will be announced. NFT Activity Coming Soon NFT Activity Coming Soon After obtaining esMOZ, users are naturally most concerned about the exchange rules. Lumoz's official documentation has previously stated that the exchange ratio between esMOZ and MOZ is 1:1, although there will be a certain unlocking period. While this setup is designed for Lumoz's long-term development, there have been voices in the community expressing a desire for an earlier unlocking. We value the feedback from every community user, which is why we have introduced an NFT section that will allow users to directly exchange a certain amount of esMOZ for MOZ. NFTs will be set at different levels, with each level offering varying esMOZ exchange limits. In the future, NFTs will also support free circulation, allowing users to exchange according to their needs for flexible asset management. We will announce the rules for obtaining NFTs shortly, so stay tuned! This airdrop marks just the beginning for Lumoz. Follow our official Twitter for updates and stay informed on our journey forward. About Lumoz About Lumoz Lumoz is a leading modular computing layer and ZK-RaaS platform, delivering computation for Rollups, ZK-ML, and ZKP verification through its PoW mining system. Earlier this year, Lumoz completed its Pre-A funding round, raising $14 million with backing from prominent investors like OKX Ventures, HashKey Capital, KuCoin Ventures, Polygon, and IDG Blockchain. With over 1.5 million users, 200+ projects, 28,137 validators, and 145 PoW miners engaged, Lumoz’s mainnet is set to launch by year’s end.

Lumoz Unveils esMOZ Airdrop Details

Lumoz revela detalles del lanzamiento aéreo de esMOZ

Lumoz Devwale Detay Airdrop esMOZ

A Lumoz bemutatja az esMOZ Airdrop részleteit

Lumoz svela i dettagli dell'airdrop esMOZ

LumozがesMOZエアドロップの詳細を発表

Lumoz បង្ហាញព័ត៌មានលម្អិត esMOZ Airdrop

Lumoz onthult esMOZ Airdrop-details

Lumoz ujawnia szczegóły zrzutu esMOZ

Lumoz Desvela esMOZ Airdrop Detalles

Lumoz zbulon detajet e esMOZ Airdrop

Lumoz тафсилоти esMOZ Airdrop-ро ошкор мекунад

Lumoz esMOZ Airdrop tafsilotlarini ochib beradi

I chose to share some aspects of my design process, as there is an abundance of fascinating content out there, ranging from general information to specific details. Sometimes, I find it valuable to compare my proсess with others and gain insights from their experiences. This text is a continuation of the previous one, related to the project I am currently working as a solo designer. I recommend that you start by spending 5 minutes reading the first article at the following link to gain more context. https://hackernoon.com/the-implementation-of-design-guidelines-for-small-and-medium-projects?embedable=true In my previous post, I discussed my experience with using design systems. The design development stage is almost finished now that this phase has been completed successfully, and I have recently begun building a website on Framer. Towards the end of the article I plan to write a couple of lines about working with Framer, but I'll go in order. Notable is a company in the engineering field that focuses on chemical product areas. The company has three distinct product lines: metering pumps, chemical pumps, and chemical reagents. The company has built a strong presence in its industry through over a decade of successful operation in the B2B market. The project's current aims include boosting brand awareness and growing the base of loyal customers. The primary issue stems from the company having three distinct operating divisions, each catering to unique target audiences and requirements. In other words, a user searching for specific information may become confused by irrelevant information, making their experience more complicated. Furthermore, the detailed technical information included in product descriptions can make it challenging to organize the visual hierarchy of content and may result in overwhelming the reader with too much information. This is how the issue was initially explained in the project. However, if you dig deeper, you will need to untangle a complex web of various questions. Gain an understanding of the user including their motives for visiting the site, the situations they are in, the questions they ask, and especially what expectations they have from the business. Conversely, the business aims to operate and grow successfully. In essence, you must strike a balance between assisting the user in navigating their current path more efficiently and aiding the business in generating revenue. Highlighting the main benefits of the service and ironing out any potential issues is essential for this task. Every business has its bottlenecks, but it's not always convenient for your client to discuss them. Based on Notable's accumulated experience, the most common ways users find a company's website have been identified: During the design phase of an object, a request is made for a specific technological solution with the involvement of key product components. The user is typically a design engineer with expertise in technology and familiarity with our products. The main benefit for the user is the ease of accessing information and navigation. It is not necessary to provide the full specification, but it is crucial to include specific key elements to ensure the designer fully grasps the requirements. The request has come up because of an unexpected incident at the facility and needs to be resolved quickly. The main focus is on navigation, providing relevant information, and establishing a clear visual hierarchy to simplify the process and guide the user. In this situation, we are not working with a designer, but instead with a manager from the procurement department. And it is probable that his technical expertise concerning the requested product will not be as extensive as it was in the prior situation. The situation occurs when a user is looking for a trustworthy business partner. In this instance, the important values are expertise, reliability, stability and brand awareness. Throughout Notable's operational period, the business has also gone through transformation. The previous top sellers have been replaced by other products. There are new solutions that have surfaced. The company naturally aims to differentiate itself in the market by creating a fresh and distinctive brand identity. In analyzing the industry and the branding of companies within similar fields, it became evident that the color balance is heavily skewed towards shades of blue. After a while, we decided to go with this color palette, which mirrors the gradient in the company logo. I believe that this decision allows us to evoke the appropriate emotions. Additionally, the logo's gradient and depth of color highlight the company's accumulated expertise over the years. Returning to the idea of making navigation easier, I thought about using this gradient on the homepage to smoothly move from hero section to the next until reaching the bottom of the page. Our target audiences vary greatly, so the solution needs to be easily understandable for all. The concept is for the user to scroll through the main page to find the relevant section, select a specific color scheme, such as Purple Heart for chemical reagents, and then continue navigating the site horizontally in this Monochromatic color scheme. The user is greeted by the start screen in Columbia Blue, followed by the dosing equipment block in Medium Purple, chemical reagents in Purple Heart, chemical pumps in Persian, and finally, Indigo tones in the footer. Therefore, the main page was designed with an Analogous color scheme to help users easily concentrate on the content they need. I also added horizontal color palettes to each block using color theory and a better sequence for the user. For instance, when browsing the Purple Heart – chemical reagents pages, the user will stay within the color scheme of the corresponding Monochromatic palette. Website structure. A decision was made to shift from a tree-like structure to a combined, linear one in order to facilitate and accelerate the user's journey. The target pages for the company's top-earning products can now be accessed with just one click. The Bento system made this possible. Cards and an improved visual hierarchy were used to highlight the top products in each Bento block. Therefore, the revised website now consists of just 25 pages, as opposed to the original 61. The achievement was partly a result of the easier-to-use content delivery system and the specific product pages. Therefore, the information architecture on Notable's corporate website now appears as follows: homepage;
Two intermediate pages;
form page;
3 key product pages;
2 service pages;
16 product pages. Three breakpoints were implemented for the main page, intermediate pages, form pages, key product pages, and service pages to ensure a responsive design: Desktop 1200 px, Tablet 1199-810 px и Phone 809 px. On Desktop, the content area is set to 1200 px; I used a simple 12-column grid with a 24 px gutter. The design for Tablet and Phone is completely adaptable. In the end, the design system I developed for this project included: 1389 components;
288 styles;
166 variants. This case is one of those where you don't have enough high quality graphic content. That's just how it turned out. It was necessary to gather everything available, to upscale in some areas, and to attempt to enhance the quality in others. It was challenging to create a large amount of content in Midjourney and other AIs because the neural network is unable to produce technical materials of this quality, this leads to numerous errors and inaccuracies, which quickly and greatly diminish the user's confidence in Notable's expertise. I edited the graphics in Photoshop, incorporated them into a unified color palette, and created the mockups. During my journey, Midjourney has been incredibly useful to me when dealing with abstract graphics representing the movement of liquid. I was fortunate in this instance as I utilized it on the home page start screens, intermediate screens, and the page featuring a key product. The style is consistent, but my main page features a light theme, the product page has a dark theme, and for the intermediate page, as a variation, I animated this abstract flow in Pika. Next I proceeded to construct the website on Framer. Initially, I perceived this stage as a specific challenge due to my lack of experience with Framer. I have experience using different website builders in the past and now have the opportunity to test this one. There is one particular drawback: the expense. Nevertheless, in all other aspects, there are only benefits at this point in time. Usually, one may face several limitations regarding the layout while collaborating with site builders. You have to pursue compromises and depart from the initial design. In the case of Framer, the process involves transferring the layout from Figma with the assistance of plugins, and the use of components is essential. Consequently, this procedure requires a certain amount of time. The animation is generated with minimal effort utilizing only a few mouse clicks. I have a sincere passion for animating layouts in Figma, and I frequently observe a point at which loading speeds noticeably decrease. However, in Framer, any layout is brought to life in a captivating and engaging manner, without requiring a significant time investment. By simply watching a couple of videos from their academy on YouTube, one can quickly gain the skills to enhance dynamic elements. Currently, I will suspend further discussion, and anticipate sharing my impressions following the release in my upcoming post. I chose to share some aspects of my design process, as there is an abundance of fascinating content out there, ranging from general information to specific details. Sometimes, I find it valuable to compare my proсess with others and gain insights from their experiences. This text is a continuation of the previous one, related to the project I am currently working as a solo designer. I recommend that you start by spending 5 minutes reading the first article at the following link to gain more context. https://hackernoon.com/the-implementation-of-design-guidelines-for-small-and-medium-projects?embedable=true https://hackernoon.com/the-implementation-of-design-guidelines-for-small-and-medium-projects?embedable=true In my previous post, I discussed my experience with using design systems. The design development stage is almost finished now that this phase has been completed successfully, and I have recently begun building a website on Framer. Towards the end of the article I plan to write a couple of lines about working with Framer, but I'll go in order. Notable is a company in the engineering field that focuses on chemical product areas. The company has three distinct product lines: metering pumps, chemical pumps, and chemical reagents. The company has built a strong presence in its industry through over a decade of successful operation in the B2B market. The project's current aims include boosting brand awareness and growing the base of loyal customers. The primary issue stems from the company having three distinct operating divisions, each catering to unique target audiences and requirements. In other words, a user searching for specific information may become confused by irrelevant information, making their experience more complicated. Furthermore, the detailed technical information included in product descriptions can make it challenging to organize the visual hierarchy of content and may result in overwhelming the reader with too much information. This is how the issue was initially explained in the project. However, if you dig deeper, you will need to untangle a complex web of various questions. Gain an understanding of the user including their motives for visiting the site, the situations they are in, the questions they ask, and especially what expectations they have from the business. Conversely, the business aims to operate and grow successfully. In essence, you must strike a balance between assisting the user in navigating their current path more efficiently and aiding the business in generating revenue. Highlighting the main benefits of the service and ironing out any potential issues is essential for this task. Every business has its bottlenecks, but it's not always convenient for your client to discuss them. Based on Notable's accumulated experience, the most common ways users find a company's website have been identified: During the design phase of an object, a request is made for a specific technological solution with the involvement of key product components. The user is typically a design engineer with expertise in technology and familiarity with our products. The main benefit for the user is the ease of accessing information and navigation. It is not necessary to provide the full specification, but it is crucial to include specific key elements to ensure the designer fully grasps the requirements. During the design phase of an object, a request is made for a specific technological solution with the involvement of key product components. The request has come up because of an unexpected incident at the facility and needs to be resolved quickly. The main focus is on navigation, providing relevant information, and establishing a clear visual hierarchy to simplify the process and guide the user. In this situation, we are not working with a designer, but instead with a manager from the procurement department. And it is probable that his technical expertise concerning the requested product will not be as extensive as it was in the prior situation. The request has come up because of an unexpected incident at the facility and needs to be resolved quickly. The situation occurs when a user is looking for a trustworthy business partner. In this instance, the important values are expertise, reliability, stability and brand awareness. The situation occurs when a user is looking for a trustworthy business partner. Throughout Notable's operational period, the business has also gone through transformation. The previous top sellers have been replaced by other products. There are new solutions that have surfaced. The company naturally aims to differentiate itself in the market by creating a fresh and distinctive brand identity. In analyzing the industry and the branding of companies within similar fields, it became evident that the color balance is heavily skewed towards shades of blue. After a while, we decided to go with this color palette, which mirrors the gradient in the company logo. I believe that this decision allows us to evoke the appropriate emotions. Additionally, the logo's gradient and depth of color highlight the company's accumulated expertise over the years. Returning to the idea of making navigation easier, I thought about using this gradient on the homepage to smoothly move from hero section to the next until reaching the bottom of the page. Our target audiences vary greatly, so the solution needs to be easily understandable for all. The concept is for the user to scroll through the main page to find the relevant section, select a specific color scheme, such as Purple Heart for chemical reagents, and then continue navigating the site horizontally in this Monochromatic color scheme. The user is greeted by the start screen in Columbia Blue, followed by the dosing equipment block in Medium Purple, chemical reagents in Purple Heart, chemical pumps in Persian, and finally, Indigo tones in the footer. Therefore, the main page was designed with an Analogous color scheme to help users easily concentrate on the content they need. I also added horizontal color palettes to each block using color theory and a better sequence for the user. For instance, when browsing the Purple Heart – chemical reagents pages, the user will stay within the color scheme of the corresponding Monochromatic palette. Website structure. A decision was made to shift from a tree-like structure to a combined, linear one in order to facilitate and accelerate the user's journey. The target pages for the company's top-earning products can now be accessed with just one click. The Bento system made this possible. Cards and an improved visual hierarchy were used to highlight the top products in each Bento block. Therefore, the revised website now consists of just 25 pages, as opposed to the original 61. The achievement was partly a result of the easier-to-use content delivery system and the specific product pages. Therefore, the information architecture on Notable's corporate website now appears as follows: homepage; Two intermediate pages; form page; 3 key product pages; 2 service pages; 16 product pages. homepage; Two intermediate pages; form page; 3 key product pages; 2 service pages; 16 product pages. Three breakpoints were implemented for the main page, intermediate pages, form pages, key product pages, and service pages to ensure a responsive design: Desktop 1200 px, Tablet 1199-810 px и Phone 809 px. On Desktop, the content area is set to 1200 px; I used a simple 12-column grid with a 24 px gutter. The design for Tablet and Phone is completely adaptable. In the end, the design system I developed for this project included: 1389 components; 288 styles; 166 variants. 1389 components; 288 styles; 166 variants. This case is one of those where you don't have enough high quality graphic content. That's just how it turned out. It was necessary to gather everything available, to upscale in some areas, and to attempt to enhance the quality in others. It was challenging to create a large amount of content in Midjourney and other AIs because the neural network is unable to produce technical materials of this quality, this leads to numerous errors and inaccuracies, which quickly and greatly diminish the user's confidence in Notable's expertise. I edited the graphics in Photoshop, incorporated them into a unified color palette, and created the mockups. During my journey, Midjourney has been incredibly useful to me when dealing with abstract graphics representing the movement of liquid. I was fortunate in this instance as I utilized it on the home page start screens, intermediate screens, and the page featuring a key product. The style is consistent, but my main page features a light theme, the product page has a dark theme, and for the intermediate page, as a variation, I animated this abstract flow in Pika. Next I proceeded to construct the website on Framer. Initially, I perceived this stage as a specific challenge due to my lack of experience with Framer. I have experience using different website builders in the past and now have the opportunity to test this one. There is one particular drawback: the expense. Nevertheless, in all other aspects, there are only benefits at this point in time. Usually, one may face several limitations regarding the layout while collaborating with site builders. You have to pursue compromises and depart from the initial design. In the case of Framer, the process involves transferring the layout from Figma with the assistance of plugins, and the use of components is essential. Consequently, this procedure requires a certain amount of time. The animation is generated with minimal effort utilizing only a few mouse clicks. I have a sincere passion for animating layouts in Figma, and I frequently observe a point at which loading speeds noticeably decrease. However, in Framer, any layout is brought to life in a captivating and engaging manner, without requiring a significant time investment. By simply watching a couple of videos from their academy on YouTube, one can quickly gain the skills to enhance dynamic elements. Currently, I will suspend further discussion, and anticipate sharing my impressions following the release in my upcoming post.

Specific Details of the Design Process

Authors:
(1) Qian Yang, Zhejiang University, Equal contribution. This work was conducted during Qian Yang’s internship at Alibaba Group;
(2) Jin Xu, Alibaba Group, Equal contribution;
(3) Wenrui Liu, Zhejiang University;
(4) Yunfei Chu, Alibaba Group;
(5) Xiaohuan Zhou, Alibaba Group;
(6) Yichong Leng, Alibaba Group;
(7) Yuanjun Lv, Alibaba Group;
(8) Zhou Zhao, Alibaba Group and Corresponding to Zhou Zhao (zhaozhou@zju.edu.cn);
(9) Yichong Leng, Zhejiang University
(10) Chang Zhou, Alibaba Group and Corresponding to Chang Zhou (ericzhou.zc@alibaba-inc.com);
(11) Jingren Zhou, Alibaba Group. Table of Links Abstract and 1. Introduction 2 Related Work 3 AIR-Bench and 3.1 Overview 3.2 Foundation Benchmark 3.3 Chat Benchmark 3.4 Evaluation Strategy 4 Experiments 4.1 Models 4.2 Main Results 4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias 5 Conclusion and References A Detailed Results of Foundation Benchmark A Detailed Results of Foundation Benchmark In Table 5, we delineate the performance assessment for each model across the various tasks on the foundation benchmark. With the exception of Speaker Gender Recognition and Synthesized Voice Detection, which are binary-choice tasks, all other tasks necessitate a selection from four options. As such, a random selection in the Speaker Gender Recognition and Synthesized Voice Detection datasets would theoretically achieve an accuracy of 50%, while the expected accuracy for random choices across the remaining datasets stands at 25%. Consequently, any performance metrics that approximate these random baselines are indicative of an absence of discernible proficiency in the respective tasks. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Qian Yang, Zhejiang University, Equal contribution. This work was conducted during Qian Yang’s internship at Alibaba Group; (2) Jin Xu, Alibaba Group, Equal contribution; (3) Wenrui Liu, Zhejiang University; (4) Yunfei Chu, Alibaba Group; (5) Xiaohuan Zhou, Alibaba Group; (6) Yichong Leng, Alibaba Group; (7) Yuanjun Lv, Alibaba Group; (8) Zhou Zhao, Alibaba Group and Corresponding to Zhou Zhao (zhaozhou@zju.edu.cn); (9) Yichong Leng, Zhejiang University (10) Chang Zhou, Alibaba Group and Corresponding to Chang Zhou (ericzhou.zc@alibaba-inc.com); (11) Jingren Zhou, Alibaba Group. Authors: Authors: (1) Qian Yang, Zhejiang University, Equal contribution. This work was conducted during Qian Yang’s internship at Alibaba Group; (2) Jin Xu, Alibaba Group, Equal contribution; (3) Wenrui Liu, Zhejiang University; (4) Yunfei Chu, Alibaba Group; (5) Xiaohuan Zhou, Alibaba Group; (6) Yichong Leng, Alibaba Group; (7) Yuanjun Lv, Alibaba Group; (8) Zhou Zhao, Alibaba Group and Corresponding to Zhou Zhao (zhaozhou@zju.edu.cn); (9) Yichong Leng, Zhejiang University (10) Chang Zhou, Alibaba Group and Corresponding to Chang Zhou (ericzhou.zc@alibaba-inc.com); (11) Jingren Zhou, Alibaba Group. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 AIR-Bench and 3.1 Overview 3 AIR-Bench and 3.1 Overview 3.2 Foundation Benchmark 3.2 Foundation Benchmark 3.3 Chat Benchmark 3.3 Chat Benchmark 3.4 Evaluation Strategy 3.4 Evaluation Strategy 4 Experiments 4.1 Models 4.1 Models 4.2 Main Results 4.2 Main Results 4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias 4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias 5 Conclusion and References 5 Conclusion and References A Detailed Results of Foundation Benchmark A Detailed Results of Foundation Benchmark A Detailed Results of Foundation Benchmark In Table 5, we delineate the performance assessment for each model across the various tasks on the foundation benchmark. With the exception of Speaker Gender Recognition and Synthesized Voice Detection, which are binary-choice tasks, all other tasks necessitate a selection from four options. As such, a random selection in the Speaker Gender Recognition and Synthesized Voice Detection datasets would theoretically achieve an accuracy of 50%, while the expected accuracy for random choices across the remaining datasets stands at 25%. Consequently, any performance metrics that approximate these random baselines are indicative of an absence of discernible proficiency in the respective tasks. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Detailed Results of the Foundation Benchmark

SAN FRANCISCO, USA / California, October 10th, 2024/Chainwire/--Constellation Network's “HyDef ‘24” conference will take place on Thursday, October 24, 2024, featuring a free daylong virtual event combined with a live in-person event for a nominal fee at 1 Hotel in San Francisco. Constellation Network is a unique Web3 framework with new open-source tooling that empowers companies and individuals to build blockchain networks for Big Data, creating trust and transparency around data collection, validation, and transacting. Nicknamed “America’s Blockchain,” Constellation Network actively works with entities such as the U.S. Military, The Digital Chamber, the Texas Blockchain Association, Space ISAC, and the National DigiFoundry. Constellation has been validated and approved by the U.S. Department of Defense through the Air Force Research Laboratory (AFRL) as a, “Scalable, Secure and Defense-Approved Blockchain technology.” “So much work has been going on behind the scenes which is why HyDef is a great forum to bring it to the forefront around one action packed event. Since the launch of Metagraphs, it's been hard to keep up with all the inbound interest from federal, enterprise and academia. We are excited to not only give updates on what has matured but also unveil new expressions that truly separate Constellation apart in the Web3 industry.” - Benjamin Diggles, Constellation Co-founder and CSO. Constellation's HyDef ‘24 conference will deliver a jam-packed day of ground-breaking insights. Conference sessions cover secure information-sharing in a zero-trust world, the future of finance and blockchain, the evolving regulatory environment, government and blockchain, and much more. Hackathon-winning projects will be showcased at the event where developers have leveraged Constellation’s big data transaction and validation capabilities to build apps that gather and validate data at scale. These apps feed the data into AI or causal models to give businesses and individuals insights based on more input than we’ve heretofore been able to process. Another HyDef highlight will be the long-awaited reveal of the details of the working relationship between Constellation and Panasonic. The work the two companies are doing together has the potential to bring Constellation’s technology and blockchain-secured edge computing to the world en masse in a meaningful way. “The Constellation Community is the backbone to our ecosystem. HyDef is a time for us all to come together, celebrate the progress in our ecosystem, give visibility to our partners, and gear up for the future of Constellation. By bringing together thought leaders, builders, and new product development we paint a picture for new opportunities in distributed networks and the communities that build them."- Ben Jorgensen, Constellation Co-founder and CEO. The in-person event will be held at the 1 Hotel in San Francisco and will require a nominal $150 fee. Attendees include representatives from Panasonic, the Greer Institute at Intel, Forward Edge AI, venture capitalists, The Digital Chamber, the Constellation leadership team, along with Stardust Collective community leaders. The virtual event is free to attend with access via livestream to all keynote speeches, panel discussions, and hackathon showcases in real time.Virtual attendees can engage directly with speakers and panelists through Q&A and live chat features, and may connect with other attendees in virtual event spaces. "Our metagraph application layer just released to the public, and this is the perfect time for us to showcase our Hackathon winners, our partners, and our progress. I'm excited for the chance to bring the industry together in this unique format and share ideas and ways we can work together and build together."- Altif Brown, Constellation co-founder, Head of Community, and conference organizer. To find out more about the event and register, users can visit stardust's event site at https://stardust-collective.org/HyDef-Conference About Constellation Network Constellation Network, founded in 2017, is a Blockchain ecosystem powered by the Hypergraph Transfer Protocol (HGTP), designed to secure, validate, and process data for Web3 applications. HGTP enables seamless and secure Blockchain communication, akin to how HTTP functions for the web. Constellation’s tools support building Blockchain networks for big data, fostering trust and transparency. Validated by the U.S. Department of Defense via the Air Force Research Laboratory (AFRL), Constellation is recognized as a scalable and secure Blockchain solution. Website: www.constellationnetwork.io Twitter: https://twitter.com/Conste11ation Telegram: https://t.me/constellationcommunity Contact Constellation co-founder, Head of Community, and conference organizer Altif Brown Constellation Network, Inc. altif@constellationnetwork.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. SAN FRANCISCO, USA / California, October 10th, 2024/Chainwire/--Constellation Network's “HyDef ‘24” conference will take place on Thursday, October 24, 2024, featuring a free daylong virtual event combined with a live in-person event for a nominal fee at 1 Hotel in San Francisco. Constellation Network is a unique Web3 framework with new open-source tooling that empowers companies and individuals to build blockchain networks for Big Data, creating trust and transparency around data collection, validation, and transacting. Nicknamed “America’s Blockchain,” Constellation Network actively works with entities such as the U.S. Military, The Digital Chamber, the Texas Blockchain Association, Space ISAC, and the National DigiFoundry. Constellation has been validated and approved by the U.S. Department of Defense through the Air Force Research Laboratory (AFRL) as a, “Scalable, Secure and Defense-Approved Blockchain technology.” “So much work has been going on behind the scenes which is why HyDef is a great forum to bring it to the forefront around one action packed event. Since the launch of Metagraphs, it's been hard to keep up with all the inbound interest from federal, enterprise and academia. We are excited to not only give updates on what has matured but also unveil new expressions that truly separate Constellation apart in the Web3 industry.” - Benjamin Diggles, Constellation Co-founder and CSO. “So much work has been going on behind the scenes which is why HyDef is a great forum to bring it to the forefront around one action packed event. Since the launch of Metagraphs, it's been hard to keep up with all the inbound interest from federal, enterprise and academia. We are excited to not only give updates on what has matured but also unveil new expressions that truly separate Constellation apart in the Web3 industry.” - Benjamin Diggles, Constellation Co-founder and CSO. Constellation's HyDef ‘24 conference will deliver a jam-packed day of ground-breaking insights. Conference sessions cover secure information-sharing in a zero-trust world, the future of finance and blockchain, the evolving regulatory environment, government and blockchain, and much more. Hackathon-winning projects will be showcased at the event where developers have leveraged Constellation’s big data transaction and validation capabilities to build apps that gather and validate data at scale. These apps feed the data into AI or causal models to give businesses and individuals insights based on more input than we’ve heretofore been able to process. Another HyDef highlight will be the long-awaited reveal of the details of the working relationship between Constellation and Panasonic. The work the two companies are doing together has the potential to bring Constellation’s technology and blockchain-secured edge computing to the world en masse in a meaningful way. “The Constellation Community is the backbone to our ecosystem. HyDef is a time for us all to come together, celebrate the progress in our ecosystem, give visibility to our partners, and gear up for the future of Constellation. By bringing together thought leaders, builders, and new product development we paint a picture for new opportunities in distributed networks and the communities that build them."- Ben Jorgensen, Constellation Co-founder and CEO. “The Constellation Community is the backbone to our ecosystem. HyDef is a time for us all to come together, celebrate the progress in our ecosystem, give visibility to our partners, and gear up for the future of Constellation. By bringing together thought leaders, builders, and new product development we paint a picture for new opportunities in distributed networks and the communities that build them."- Ben Jorgensen, Constellation Co-founder and CEO. The in-person event will be held at the 1 Hotel in San Francisco and will require a nominal $150 fee. Attendees include representatives from Panasonic, the Greer Institute at Intel, Forward Edge AI, venture capitalists, The Digital Chamber, the Constellation leadership team, along with Stardust Collective community leaders. The virtual event is free to attend with access via livestream to all keynote speeches, panel discussions, and hackathon showcases in real time.Virtual attendees can engage directly with speakers and panelists through Q&A and live chat features, and may connect with other attendees in virtual event spaces. "Our metagraph application layer just released to the public, and this is the perfect time for us to showcase our Hackathon winners, our partners, and our progress. I'm excited for the chance to bring the industry together in this unique format and share ideas and ways we can work together and build together."- Altif Brown, Constellation co-founder, Head of Community, and conference organizer. "Our metagraph application layer just released to the public, and this is the perfect time for us to showcase our Hackathon winners, our partners, and our progress. I'm excited for the chance to bring the industry together in this unique format and share ideas and ways we can work together and build together."- Altif Brown, Constellation co-founder, Head of Community, and conference organizer. To find out more about the event and register, users can visit stardust's event site at https://stardust-collective.org/HyDef-Conference https://stardust-collective.org/HyDef-Conference https://stardust-collective.org/HyDef-Conference About Constellation Network Constellation Network , founded in 2017, is a Blockchain ecosystem powered by the Hypergraph Transfer Protocol (HGTP), designed to secure, validate, and process data for Web3 applications. Constellation Network Constellation Network HGTP enables seamless and secure Blockchain communication, akin to how HTTP functions for the web. Constellation’s tools support building Blockchain networks for big data, fostering trust and transparency. Validated by the U.S. Department of Defense via the Air Force Research Laboratory (AFRL), Constellation is recognized as a scalable and secure Blockchain solution. Website: www.constellationnetwork.io www.constellationnetwork.io www.constellationnetwork.io Twitter: https://twitter.com/Conste11ation https://twitter.com/Conste11ation https://twitter.com/Conste11ation Telegram: https://t.me/constellationcommunity https://t.me/constellationcommunity https://t.me/constellationcommunity Contact Constellation co-founder, Head of Community, and conference organizer Altif Brown Constellation Network, Inc. altif@constellationnetwork.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. here . here

Constellation Network, The DoD-vetted Blockchain For Big Data, Unveils Panasonic Partnership Details

Constellation Network, правераны Міністэрствам абароны блокчэйн для вялікіх даных, раскрывае падрабязнасці партнёрства Panasonic

Constellation Network, la cadena de blocs aprovada pel DoD per a grans dades, revela els detalls de l'associació de Panasonic

Constellation Network, la cadena de bloques aprobada por el Departamento de Defensa para Big Data, revela detalles de su asociación con Panasonic

Constellation Network, Blockchain yang Diuji DoD untuk Big Data, Mengungkap Detail Kemitraan Panasonic

国防総省認定のビッグデータ向けブロックチェーン、コンステレーションネットワークがパナソニックとの提携の詳細を発表

Constellation Network, DoD тарабынан текшерилген чоң маалыматтар үчүн блокчейн, Panasonic өнөктөштүк чоо-жайын ачат

Tambajotra Constellation, Blockchain nohamarinin'ny DoD ho an'ny angon-drakitra lehibe, dia manambara ny antsipiriany momba ny fiaraha-miasa amin'ny Panasonic

Constellation Network, de door het Amerikaanse ministerie van Defensie goedgekeurde blockchain voor big data, onthult details over partnerschap met Panasonic

Constellation Network, den DoD-bevakade blockkedjan för Big Data, avslöjar detaljer om Panasonics partnerskap

Шабакаи Constellation, Blockchain аз ҷониби DoD санҷидашуда барои маълумоти калон, тафсилоти шарикии Panasonic -ро ошкор мекунад

Styldyzlar tory, Uly maglumatlar üçin DoD-barlagly blokchein, Panasonic Partnership Jikme-jikliklerini açýar

Inethiwekhi yeConstellation, iBlockchain ehlolwe yiDoD yeDatha enkulu, ityhila iiNkcukacha zoBudlelwane bePanasonic

Authors:
(1) Liang Wang, Microsoft Corporation, and Correspondence to (wangliang@microsoft.com);
(2) Nan Yang, Microsoft Corporation, and correspondence to (nanya@microsoft.com);
(3) Xiaolong Huang, Microsoft Corporation;
(4) Linjun Yang, Microsoft Corporation;
(5) Rangan Majumder, Microsoft Corporation;
(6) Furu Wei, Microsoft Corporation and Correspondence to (fuwei@microsoft.com). Table of Links Abstract and 1 Introduction 2 Related Work 3 Method 3.1 Synthetic Data Generation 3.2 Training 4 Experiments 4.1 Statistics of the Synthetic Data 4.2 Model Fine-tuning and Evaluation 4.3 Main Results 4.4 Multilingual Retrieval 5 Analysis 5.1 Is Contrastive Pre-training Necessary? 5.2 Extending to Long Text Embeddings and 5.3 Analysis of Training Hyperparameters 6 Conclusion and References A Implementation Details B Test Set Contamination Analysis C Prompts for Synthetic Data Generation D Instructions for Training and Evaluation A Implementation Details The model and dataset release information is available at https://github.com/microsoft/ unilm/tree/master/e5. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Liang Wang, Microsoft Corporation, and Correspondence to (wangliang@microsoft.com); (2) Nan Yang, Microsoft Corporation, and correspondence to (nanya@microsoft.com); (3) Xiaolong Huang, Microsoft Corporation; (4) Linjun Yang, Microsoft Corporation; (5) Rangan Majumder, Microsoft Corporation; (6) Furu Wei, Microsoft Corporation and Correspondence to (fuwei@microsoft.com). Authors: Authors: (1) Liang Wang, Microsoft Corporation, and Correspondence to (wangliang@microsoft.com); (2) Nan Yang, Microsoft Corporation, and correspondence to (nanya@microsoft.com); (3) Xiaolong Huang, Microsoft Corporation; (4) Linjun Yang, Microsoft Corporation; (5) Rangan Majumder, Microsoft Corporation; (6) Furu Wei, Microsoft Corporation and Correspondence to (fuwei@microsoft.com). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Method 3.1 Synthetic Data Generation 3.1 Synthetic Data Generation 3.2 Training 3.2 Training 4 Experiments 4.1 Statistics of the Synthetic Data 4.1 Statistics of the Synthetic Data 4.2 Model Fine-tuning and Evaluation 4.2 Model Fine-tuning and Evaluation 4.3 Main Results 4.3 Main Results 4.4 Multilingual Retrieval 4.4 Multilingual Retrieval 5 Analysis 5.1 Is Contrastive Pre-training Necessary? 5.1 Is Contrastive Pre-training Necessary? 5.2 Extending to Long Text Embeddings and 5.3 Analysis of Training Hyperparameters 5.2 Extending to Long Text Embeddings and 5.3 Analysis of Training Hyperparameters 6 Conclusion and References 6 Conclusion and References A Implementation Details A Implementation Details B Test Set Contamination Analysis B Test Set Contamination Analysis C Prompts for Synthetic Data Generation C Prompts for Synthetic Data Generation D Instructions for Training and Evaluation D Instructions for Training and Evaluation A Implementation Details The model and dataset release information is available at https://github.com/microsoft/ unilm/tree/master/e5. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

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