惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

罗磊的独立博客
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - 三生石上(FineUI控件)
Hugging Face - Blog
Hugging Face - Blog
小众软件
小众软件
量子位
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
IT之家
IT之家
V
V2EX
阮一峰的网络日志
阮一峰的网络日志
爱范儿
爱范儿
人人都是产品经理
人人都是产品经理
博客园 - Franky
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园_首页
V
Visual Studio Blog
Last Week in AI
Last Week in AI
有赞技术团队
有赞技术团队
WordPress大学
WordPress大学
博客园 - 司徒正美
大猫的无限游戏
大猫的无限游戏
博客园 - 【当耐特】
MyScale Blog
MyScale Blog
月光博客
月光博客

cs.DC updates on arXiv.org

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
Anchoring the value of Cryptocurrency
Yibin Xu, Yangyu Huang, Jianhua Shao · 2020-01-19 · via cs.DC updates on arXiv.org

A decade long thrive of cryptocurrency has shown its potential as a source of alternative-finance and the security and the robustness of the underpinning blockchain technology. However, most cryptocurrencies fail to show inimitability and their meanings in the real world. As a result, they usually start off as favourites but quickly become the outcasts of the digital asset market. The blockchain society attempts to anchor the value of cryptocurrency with real values by employing smart contracts and link it with computation resources and the digital-productivity that have value and demands in the real world. But their attempts have some undesirable effects due to a limited number of practical applications. This limitation is caused by the dilemma between high performance and decentralisation (universal joinability). The emerging of blockchain sharding models, however, has offered a possible solution to address this dilemma. In this paper, we explore a financial model for blockchain sharding that will build an active link between the value of cryptocurrency and computation resources as well as the market and labour behaviours. Our model can adjust the price of resources and the compensation for maintaining a system based on those behaviours. We anchor the value of cryptocurrency by the amount of computation resources participated in and give the cryptocurrency a meaning as the exchange between computation resources globally. Finally, we present a working example which, through financial regularities, regulates the behaviour of anonymous participants, also incents/discourages participation dynamically.