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DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Measurement, Analysis, and Insight of NFTs Transaction Ne...
Prakhyat Khati · 2022-11-29 · via cs.DC updates on arXiv.org

Non-fungible tokens (NFTs) are unique digital items with blockchain managed ownership. Ethereum blockchain based smart contract created the environment for NFTs (ERC721) to reach its one of the most important future application domains. Non fungible tokens got more attention when the market saw record breaking sales in 2021. Virtually anything of value can be traced and traded on the blockchain network by minting them as NFTs. NFTs provide the users with a decentralized proof of ownership representation, as every transaction and trade of NFTs gets recorded in the Ethereum network blocks. The value of NFTs is derived from their being non fungible meaning that the token cannot be replaced with an identical token (giving it inherent scarcity). In this paper, we study the growth rate and evolutionary nature of the NFT network and try to understand the NFT ecosystem. We explore the evolving nature of the NFT interaction network from a temporal graph perspective. We study the growth rate and observer the semantics of the network. Here on the observer network, we will run two graph algorithms on the dataset. Lastly, observe and forecast the survival of NFTs bubble by applying the Logarithmic periodic power law (LPPL) model to the time series data on one of the most famous NFT collections CryptoPunks (predicting price increase), which has seen sales of around $23.7 million around mid of 2021.